libNeuroML: documentation¶

Welcome to the libNeuroML documentation. Here you will find information on installing, using, and contributing to libNeuroML. For more information on NeuroML standard, other tools in the NeuroML eco-system, the NeuroML community and how to get in touch with us, please see the documentation at https://docs.neuroml.org.

User guide¶

Introduction¶

This package provides Python libNeuroML, for working with neuronal models specified in NeuroML 2.

Warning

libNeuroML targets NeuroML v2.0

libNeuroML targets NeuroML v2.0, which is described in Cannon et al, 2014). NeuroML v1.8.1 (Gleeson et al. 2010) is now deprecated and not supported by libNeuroML.

For a detailed description of libNeuroML see Vella et al. [VCC+14]. Please cite the paper if you use libNeuroML.

NeuroML¶

NeuroML provides an object model for describing neuronal morphologies, ion channels, synapses and 3D network structure. For more information on NeuroML 2 and LEMS please see the NeuroML documentation.

Serialisations¶

The XML serialisation will be the “natural” serialisation and will follow closely the NeuroML object model. The format of the XML will be specified by the XML Schema definition (XSD file).

Other serialisations have been developed (HDF5, JSON, SWC). Please see Vella et al. [VCC+14] for more details.

Installation¶

Using Pip¶

On most systems with a Python installation, libNeuroML can be installed using the default Python package manager, Pip:

pip install libNeuroML

It is recommended to use a virtual environment when installing Python packages using pip to prevent these from conflicting with other system libraries.

This will support the default XML serialization. To install all of requirements to include the other serialisations, use

# On Ubuntu based systems
sudo apt-get install libhdf5-dev
pip install libNeuroML[full]

The apt line is required at time of writing because PyTables’ wheels for python 3.7 depend on the system libhdf5.

On Fedora based systems¶

On Fedora Linux systems, the NeuroFedora community provides libNeuroML in the standard Fedora repos and can be installed using the following commands:

sudo dnf install python3-libNeuroML

Install from source¶

You can clone the GitHub repository and also build libNeuroML from the sources. For this, you will need git:

git clone git://github.com/NeuralEnsemble/libNeuroML.git
cd libNeuroML

More details about the git repository and making your own branch/fork are here. To build and install libNeuroML, you can use the standard install method for Python packages (preferably in a virtual environment):

python setup.py install

To use the latest development version of libNeuroML, switch to the development branch:

git checkout development
sudo python setup.py install

Run an example¶

Some sample scripts are included in neuroml/examples, e.g. :

cd neuroml/examples
python build_network.py

The standard examples can also be found Examples.

Unit tests¶

To run unit tests cd to the directory neuroml/test and use the Python unittest module discover method:

cd neuroml/test/
python -m unittest discover

If all tests passed correctly, your output should look something like this:

.......
----------------------------------------------------------------------
Ran 55 tests in 40.1s

OK

You can also use PyTest to run tests.

pip install pytest
pytest -v --strict -W all

To ignore some tests, like the MongoDB test which requires a MongoDB setup, run:

pytest -v -k "not mongodb" --strict -W all

API documentation¶

The libNeuroML API includes the core NeuroML classes and various utilities. You can find information on these in the pages below.

`nml` Module (NeuroML Core classes)¶

These NeuroML core classes are Python representations of the Component Types defined in the NeuroML standard . These can be used to build NeuroML models in Python, and these models can then be exported to the standard XML NeuroML representation. These core classes also contain some utility functions to make it easier for users to carry out common tasks.

Each NeuroML Component Type is represented here as a Python class. Due to implementation limitations, whereas NeuroML Component Types use lower camel case naming, the Python classes here use upper camel case naming. So, for example, the adExIaFCell Component Type in the NeuroML schema becomes the AdExIaFCell class here, and expTwoSynapse becomes the ExpTwoSynapse class.

The child and children elements that NeuroML Component Types can have are represented in the Python classes as variables. The variable names, to distinguish them from class names, use snake case. So for example, the cell NeuroML Component Type has a corresponding Cell Python class here. The biophysicalProperties child Component Type in cell is represented as the biophysical_properties list variable in the Cell Python class. The class signatures list all the child/children elements and text fields that the corresponding Component Type possesses. To again use the Cell class as an example, the construction signature is this:

class neuroml.nml.nml.Cell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, morphology_attr=None, biophysical_properties_attr=None, morphology=None, biophysical_properties=None, extensiontype_=None, **kwargs_)

As can be seen here, it includes both the biophysical_properties and morphology child elements as variables.

Please see the examples in the NeuroML documentation to see usage examples of libNeuroML. Please also note that this module is also included in the top level of the neuroml package, so you can use these classes by importing neuroml:

from neuroml import AdExIaFCell

List of Component classes¶

This documentation is auto-generated from the NeuroML schema. In case of issues, please refer to the schema documentation for clarifications. If the schema documentation does not resolve the issue, please contact us.

AdExIaFCell¶

class neuroml.nml.nml.AdExIaFCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, C=None, g_l=None, EL=None, reset=None, VT=None, thresh=None, del_t=None, tauw=None, refract=None, a=None, b=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCellMembPotCap

AdExIaFCell – Model based on Brette R and Gerstner W ( 2005 ) Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity. J Neurophysiol 94:3637-3642

Parameters

gL (conductance) –
EL (voltage) –
VT (voltage) –
thresh (voltage) –
reset (voltage) –
delT (voltage) –
tauw (time) –
refract (time) –
a (conductance) –
b (current) –
C (capacitance) – Total capacitance of the cell membrane

AlphaCondSynapse¶

class neuroml.nml.nml.AlphaCondSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, e_rev=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BasePynnSynapse

AlphaCondSynapse – Alpha synapse: rise time and decay time are both tau_syn. Conductance based synapse.

Parameters

e_rev (none) –
tau_syn (none) –

AlphaCurrSynapse¶

class neuroml.nml.nml.AlphaCurrSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BasePynnSynapse

AlphaCurrSynapse – Alpha synapse: rise time and decay time are both tau_syn. Current based synapse.

Parameters: tau_syn (none) –

AlphaCurrentSynapse¶

class neuroml.nml.nml.AlphaCurrentSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau=None, ibase=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCurrentBasedSynapse

AlphaCurrentSynapse – Alpha current synapse: rise time and decay time are both tau.

Parameters

tau (time) – Time course for rise and decay
ibase (current) – Baseline current increase after receiving a spike

AlphaSynapse¶

class neuroml.nml.nml.AlphaSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, tau=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConductanceBasedSynapse

AlphaSynapse – Ohmic synapse model where rise time and decay time are both tau. Max conductance reached during this time ( assuming zero conductance before ) is gbase * weight.

Parameters

tau (time) – Time course of rise/decay
gbase (conductance) – Baseline conductance, generally the maximum conductance following a single spike
erev (voltage) – Reversal potential of the synapse

Annotation¶

class neuroml.nml.nml.Annotation(anytypeobjs_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Annotation – A structured annotation containing metadata, specifically RDF or property elements

Base¶

class neuroml.nml.nml.Base(neuro_lex_id=None, id=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseWithoutId

Base – Anything which can have a unique (within its parent) id of the form NmlId (spaceless combination of letters, numbers and underscore).

BaseCell¶

class neuroml.nml.nml.BaseCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

BaseCell – Base type of any cell ( e. g. point neuron like izhikevich2007Cell , or a morphologically detailed Cell with segment s ) which can be used in a population

BaseCellMembPotCap¶

class neuroml.nml.nml.BaseCellMembPotCap(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, C=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

BaseCellMembPotCap – Any cell with a membrane potential v with voltage units and a membrane capacitance C. Also defines exposed value iSyn for current due to external synapses and iMemb for total transmembrane current ( usually channel currents plus iSyn )

Parameters: C (capacitance) – Total capacitance of the cell membrane

BaseConductanceBasedSynapse¶

class neuroml.nml.nml.BaseConductanceBasedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseVoltageDepSynapse

BaseConductanceBasedSynapse – Synapse model which exposes a conductance g in addition to producing a current. Not necessarily ohmic!! cno_0000027

Parameters

gbase (conductance) – Baseline conductance, generally the maximum conductance following a single spike
erev (voltage) – Reversal potential of the synapse

BaseConductanceBasedSynapseTwo¶

class neuroml.nml.nml.BaseConductanceBasedSynapseTwo(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase1=None, gbase2=None, erev=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseVoltageDepSynapse

BaseConductanceBasedSynapseTwo – Synapse model suited for a sum of two expTwoSynapses which exposes a conductance g in addition to producing a current. Not necessarily ohmic!! cno_0000027

Parameters

gbase1 (conductance) – Baseline conductance 1
gbase2 (conductance) – Baseline conductance 2
erev (voltage) – Reversal potential of the synapse

BaseConnection¶

class neuroml.nml.nml.BaseConnection(neuro_lex_id=None, id=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseNonNegativeIntegerId

BaseConnection – Base of all synaptic connections (chemical/electrical/analog, etc.) inside projections

BaseConnectionNewFormat¶

class neuroml.nml.nml.BaseConnectionNewFormat(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConnection

BaseConnectionNewFormat – Base of all synaptic connections with preCell, postSegment, etc. See BaseConnectionOldFormat

BaseConnectionOldFormat¶

class neuroml.nml.nml.BaseConnectionOldFormat(neuro_lex_id=None, id=None, pre_cell_id=None, pre_segment_id='0', pre_fraction_along='0.5', post_cell_id=None, post_segment_id='0', post_fraction_along='0.5', extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConnection

BaseConnectionOldFormat – Base of all synaptic connections with preCellId, postSegmentId, etc. Note: this is not the best name for these attributes, since Id is superfluous, hence BaseConnectionNewFormat

BaseCurrentBasedSynapse¶

class neuroml.nml.nml.BaseCurrentBasedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

BaseCurrentBasedSynapse – Synapse model which produces a synaptic current.

BaseNonNegativeIntegerId¶

class neuroml.nml.nml.BaseNonNegativeIntegerId(neuro_lex_id=None, id=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseWithoutId

BaseNonNegativeIntegerId – Anything which can have a unique (within its parent) id, which must be an integer zero or greater.

BaseProjection¶

class neuroml.nml.nml.BaseProjection(neuro_lex_id=None, id=None, presynaptic_population=None, postsynaptic_population=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

BaseProjection – Base for projection (set of synaptic connections) between two populations

BasePynnSynapse¶

class neuroml.nml.nml.BasePynnSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

BasePynnSynapse – Base type for all PyNN synapses. Note, the current I produced is dimensionless, but it requires a membrane potential v with dimension voltage

Parameters: tau_syn (none) –

BaseSynapse¶

class neuroml.nml.nml.BaseSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

BaseSynapse – Base type for all synapses, i. e. ComponentTypes which produce a current ( dimension current ) and change Dynamics in response to an incoming event. cno_0000009

BaseVoltageDepSynapse¶

class neuroml.nml.nml.BaseVoltageDepSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

BaseVoltageDepSynapse – Base type for synapses with a dependence on membrane potential

BaseWithoutId¶

class neuroml.nml.nml.BaseWithoutId(neuro_lex_id=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

BaseWithoutId – Base element without ID specified yet, e.g. for an element with a particular requirement on its id which does not comply with NmlId (e.g. Segment needs nonNegativeInteger).

add(obj=None, hint=None, force=False)¶

Generic function to allow easy addition of a new member to a NeuroML object.

Without arguments, when obj=None, it simply calls the info() method to provide the list of valid member types for the NeuroML class.

Use info(show_contents=True) to see the valid members of this class, and their current contents.

Parameters

obj (any NeuroML Type defined by the API) – object member to add
hint (string) – member name to add to when there are multiple members that obj can be added to
force (bool) – boolean to force addition when an obj has already been added previously

Raises

Exception – if a member compatible to obj could not be found
Exception – if multiple members can accept the object and no hint is provided.

get_members()¶

Get member data items, also from ancestors.

This function is required because generateDS does not include inherited members in the member_data_items list for a derived class. So, for example, while IonChannelHH has gate_hh_rates which it inherits from IonChannel, IonChannelHH’s member_data_items_ is empty. It relies on the IonChannel classes’ member_data_items_ list.

Returns: list of members, including ones inherited from ancestors.

info(show_contents=False)¶

A helper function to get a list of members of this class.

This is useful to quickly check what members can go into a particular NeuroML class (which will match the Schema definitions). It lists these members and notes whether they are “single” type elements (Child elements) or “List” elements (Children elements). It will also note whether a member is optional or required.

See http://www.davekuhlman.org/generateDS.html#user-methods for more information on the MemberSpec_ class that generateDS uses.

Parameters: show_contents (bool) – also prints out the contents of the members
Returns: the string (for testing purposes)

BiophysicalProperties¶

class neuroml.nml.nml.BiophysicalProperties(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, membrane_properties=None, intracellular_properties=None, extracellular_properties=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

BiophysicalProperties – The biophysical properties of the cell , including the membraneProperties and the intracellularProperties

BiophysicalProperties2CaPools¶

class neuroml.nml.nml.BiophysicalProperties2CaPools(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, membrane_properties2_ca_pools=None, intracellular_properties2_ca_pools=None, extracellular_properties=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

BiophysicalProperties2CaPools – The biophysical properties of the cell , including the membraneProperties2CaPools and the intracellularProperties2CaPools for a cell with two Ca pools

BlockMechanism¶

class neuroml.nml.nml.BlockMechanism(type=None, species=None, block_concentration=None, scaling_conc=None, scaling_volt=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

BlockingPlasticSynapse¶

class neuroml.nml.nml.BlockingPlasticSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, tau_decay=None, tau_rise=None, plasticity_mechanism=None, block_mechanism=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ExpTwoSynapse

BlockingPlasticSynapse – Biexponential synapse that allows for optional block and plasticity mechanisms, which can be expressed as child elements.

Parameters

tauRise (time) –
tauDecay (time) –
gbase (conductance) – Baseline conductance, generally the maximum conductance following a single spike
erev (voltage) – Reversal potential of the synapse

Case¶

class neuroml.nml.nml.Case(condition=None, value=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

Cell¶

class neuroml.nml.nml.Cell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, morphology_attr=None, biophysical_properties_attr=None, morphology=None, biophysical_properties=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

Cell – Cell with segment s specified in a morphology element along with details on its biophysicalProperties . NOTE: this can only be correctly simulated using jLEMS when there is a single segment in the cell, and v of this cell represents the membrane potential in that isopotential segment.

get_actual_proximal(segment_id)¶

Get the proximal point of a segment.

Get the proximal point of a segment, even the proximal field is None and so the proximal point is on the parent (at a point set by fraction_along).

Parameters: segment_id – ID of segment
Returns: proximal point

get_all_segments_in_group(segment_group, assume_all_means_all=True)¶

Get all the segments in a segment group of the cell.

Parameters

segment_group – segment group to get all segments of
assume_all_means_all – return all segments if the segment group wasn’t explicitly defined

Todo

check docstring

Returns

list of segments

Raises

Exception – if no segment group is found in the cell.

get_ordered_segments_in_groups(group_list, check_parentage=False, include_cumulative_lengths=False, include_path_lengths=False, path_length_metric='Path Length from root')¶

Get ordered list of segments in specified groups

Parameters

group_list – list of groups to get segments from
check_parentage – verify parentage
include_commulative_lengths – also include cummulative lengths
include_path_lengths – also include path lengths
path_length_metric –

Returns

dictionary of segments with additional information depending on what parameters were used:

Raises

Exception if check_parentage is True and parentage cannot be verified

get_segment(segment_id)¶

Get segment object by its id

Parameters: segment_id – ID of segment
Returns: segment
Raises: Exception – if the segment is not found in the cell

get_segment_group(sg_id)¶

Return the SegmentGroup object for the specified segment group id.

Parameters: sg_id (str) – id of segment group to find
Returns: SegmentGroup object of specified ID
Raises: Exception – if segment group is not found in cell

get_segment_groups_by_substring(substring)¶

Get a dictionary of segment group IDs and the segment groups matching the specified substring

Parameters: substring (str) – substring to match
Returns: dictionary with segment group ID as key, and segment group as value
Raises: Exception – if no segment groups are not found in cell

get_segment_ids_vs_segments()¶

Get a dictionary of segment IDs and the segments in the cell.

Returns: dictionary with segment ID as key, and segment as value

get_segment_length(segment_id)¶

Get the length of the segment.

Parameters: segment_id – ID of segment
Returns: length of segment

get_segment_surface_area(segment_id)¶

Get the surface area of the segment.

Parameters: segment_id – ID of the segment
Returns: surface area of segment

get_segment_volume(segment_id)¶

Get volume of segment

Parameters: segment_id – ID of the segment
Returns: volume of the segment

get_segments_by_substring(substring)¶

Get a dictionary of segment IDs and the segment matching the specified substring

Parameters: substring (str) – substring to match
Returns: dictionary with segment ID as key, and segment as value
Raises: Exception – if no segments are found

summary()¶: Print cell summary.

Cell2CaPools¶

class neuroml.nml.nml.Cell2CaPools(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, morphology_attr=None, biophysical_properties_attr=None, morphology=None, biophysical_properties=None, biophysical_properties2_ca_pools=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Cell

Cell2CaPools – Variant of cell with two independent Ca2+ pools. Cell with segment s specified in a morphology element along with details on its biophysicalProperties . NOTE: this can only be correctly simulated using jLEMS when there is a single segment in the cell, and v of this cell represents the membrane potential in that isopotential segment.

CellSet¶

class neuroml.nml.nml.CellSet(neuro_lex_id=None, id=None, select=None, anytypeobjs_=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.Base

ChannelDensity¶

class neuroml.nml.nml.ChannelDensity(neuro_lex_id=None, id=None, ion_channel=None, cond_density=None, erev=None, segment_groups='all', segments=None, ion=None, variable_parameters=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensity – Specifies a time varying ohmic conductance density, gDensity, which is distributed on an area of the cell ( specified in membraneProperties ) with fixed reversal potential erev producing a current density iDensity

Parameters

erev (voltage) – The reversal potential of the current produced
condDensity (conductanceDensity) –

ChannelDensityGHK¶

class neuroml.nml.nml.ChannelDensityGHK(neuro_lex_id=None, id=None, ion_channel=None, permeability=None, segment_groups='all', segments=None, ion=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensityGHK – Specifies a time varying conductance density, gDensity, which is distributed on an area of the cell, producing a current density iDensity and whose reversal potential is calculated from the Goldman Hodgkin Katz equation. Hard coded for Ca only! See https://github.com/OpenSourceBrain/ghk-nernst.

Parameters: permeability (permeability) –

ChannelDensityGHK2¶

class neuroml.nml.nml.ChannelDensityGHK2(neuro_lex_id=None, id=None, ion_channel=None, cond_density=None, segment_groups='all', segments=None, ion=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensityGHK2 – Time varying conductance density, gDensity, which is distributed on an area of the cell, producing a current density iDensity. Modified version of Jaffe et al. 1994 ( used also in Lawrence et al. 2006 ). See https://github.com/OpenSourceBrain/ghk-nernst.

Parameters: condDensity (conductanceDensity) –

ChannelDensityNernst¶

class neuroml.nml.nml.ChannelDensityNernst(neuro_lex_id=None, id=None, ion_channel=None, cond_density=None, segment_groups='all', segments=None, ion=None, variable_parameters=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensityNernst – Specifies a time varying conductance density, gDensity, which is distributed on an area of the cell, producing a current density iDensity and whose reversal potential is calculated from the Nernst equation. Hard coded for Ca only! See https://github.com/OpenSourceBrain/ghk-nernst.

Parameters: condDensity (conductanceDensity) –

ChannelDensityNernstCa2¶

class neuroml.nml.nml.ChannelDensityNernstCa2(neuro_lex_id=None, id=None, ion_channel=None, cond_density=None, segment_groups='all', segments=None, ion=None, variable_parameters=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ChannelDensityNernst

ChannelDensityNernstCa2 – This component is similar to the original component type channelDensityNernst but it is changed in order to have a reversal potential that depends on a second independent Ca++ pool ( ca2 ). See https://github.com/OpenSourceBrain/ghk-nernst.

Parameters: condDensity (conductanceDensity) –

ChannelDensityNonUniform¶

class neuroml.nml.nml.ChannelDensityNonUniform(neuro_lex_id=None, id=None, ion_channel=None, erev=None, ion=None, variable_parameters=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensityNonUniform – Specifies a time varying ohmic conductance density, which is distributed on a region of the cell. The conductance density of the channel is not uniform, but is set using the variableParameter . Note, there is no dynamical description of this in LEMS yet, as this type only makes sense for multicompartmental cells. A ComponentType for this needs to be present to enable export of NeuroML 2 multicompartmental cells via LEMS/jNeuroML to NEURON

Parameters: erev (voltage) – The reversal potential of the current produced

ChannelDensityNonUniformGHK¶

class neuroml.nml.nml.ChannelDensityNonUniformGHK(neuro_lex_id=None, id=None, ion_channel=None, ion=None, variable_parameters=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensityNonUniformGHK – Specifies a time varying conductance density, which is distributed on a region of the cell, and whose current is calculated from the Goldman-Hodgkin-Katz equation. Hard coded for Ca only!. The conductance density of the channel is not uniform, but is set using the variableParameter . Note, there is no dynamical description of this in LEMS yet, as this type only makes sense for multicompartmental cells. A ComponentType for this needs to be present to enable export of NeuroML 2 multicompartmental cells via LEMS/jNeuroML to NEURON

ChannelDensityNonUniformNernst¶

class neuroml.nml.nml.ChannelDensityNonUniformNernst(neuro_lex_id=None, id=None, ion_channel=None, ion=None, variable_parameters=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelDensityNonUniformNernst – Specifies a time varying conductance density, which is distributed on a region of the cell, and whose reversal potential is calculated from the Nernst equation. Hard coded for Ca only!. The conductance density of the channel is not uniform, but is set using the variableParameter . Note, there is no dynamical description of this in LEMS yet, as this type only makes sense for multicompartmental cells. A ComponentType for this needs to be present to enable export of NeuroML 2 multicompartmental cells via LEMS/jNeuroML to NEURON

ChannelDensityVShift¶

class neuroml.nml.nml.ChannelDensityVShift(neuro_lex_id=None, id=None, ion_channel=None, cond_density=None, erev=None, segment_groups='all', segments=None, ion=None, variable_parameters=None, v_shift=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ChannelDensity

ChannelDensityVShift – Same as channelDensity , but with a vShift parameter to change voltage activation of gates. The exact usage of vShift in expressions for rates is determined by the individual gates.

Parameters

vShift (voltage) –
erev (voltage) – The reversal potential of the current produced
condDensity (conductanceDensity) –

ChannelPopulation¶

class neuroml.nml.nml.ChannelPopulation(neuro_lex_id=None, id=None, ion_channel=None, number=None, erev=None, segment_groups='all', segments=None, ion=None, variable_parameters=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ChannelPopulation – Population of a number of ohmic ion channels. These each produce a conductance channelg across a reversal potential erev, giving a total current i. Note that active membrane currents are more frequently specified as a density over an area of the cell using channelDensity

Parameters

number (none) – The number of channels present. This will be multiplied by the time varying conductance of the individual ion channel ( which extends baseIonChannel ) to produce the total conductance
erev (voltage) – The reversal potential of the current produced

ClosedState¶

class neuroml.nml.nml.ClosedState(neuro_lex_id=None, id=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ClosedState – A KSState with relativeConductance of 0

Parameters: relativeConductance (none) –

ComponentType¶

class neuroml.nml.nml.ComponentType(name=None, extends=None, description=None, Property=None, Parameter=None, Constant=None, Exposure=None, Requirement=None, InstanceRequirement=None, Dynamics=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

ComponentType – Contains an extension to NeuroML by creating custom LEMS ComponentType.

CompoundInput¶

class neuroml.nml.nml.CompoundInput(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, pulse_generators=None, sine_generators=None, ramp_generators=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

CompoundInput – Generates a current which is the sum of all its child basePointCurrent element, e. g. can be a combination of pulseGenerator , sineGenerator elements producing a single i. Scaled by weight, if set

CompoundInputDL¶

class neuroml.nml.nml.CompoundInputDL(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, pulse_generator_dls=None, sine_generator_dls=None, ramp_generator_dls=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

CompoundInputDL – Generates a current which is the sum of all its child basePointCurrentDL elements, e. g. can be a combination of pulseGeneratorDL , sineGeneratorDL elements producing a single i. Scaled by weight, if set

ConcentrationModel_D¶

class neuroml.nml.nml.ConcentrationModel_D(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, ion=None, resting_conc=None, decay_constant=None, shell_thickness=None, type='decayingPoolConcentrationModel', gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.DecayingPoolConcentrationModel

ConditionalDerivedVariable¶

class neuroml.nml.nml.ConditionalDerivedVariable(name=None, dimension=None, description=None, exposure=None, Case=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.NamedDimensionalVariable

ConditionalDerivedVariable – LEMS ComponentType for ConditionalDerivedVariable

Connection¶

class neuroml.nml.nml.Connection(neuro_lex_id=None, id=None, pre_cell_id=None, pre_segment_id='0', pre_fraction_along='0.5', post_cell_id=None, post_segment_id='0', post_fraction_along='0.5', gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConnectionOldFormat

Connection – Event connection directly between named components, which gets processed via a new instance of a synapse component which is created on the target component. Normally contained inside a projection element.

get_post_cell_id()¶

Get the ID of the post-synaptic cell

Returns: ID of post-synaptic cell
Return type: str

get_post_fraction_along()¶: Get post-synaptic fraction along information

get_post_info()¶: Get post-synaptic information summary

get_post_segment_id()¶

Get the ID of the post-synpatic segment

Returns: ID of post-synaptic segment.
Return type: str

get_pre_cell_id()¶

Get the ID of the pre-synaptic cell

Returns: ID of pre-synaptic cell
Return type: str

get_pre_fraction_along()¶: Get pre-synaptic fraction along information

get_pre_info()¶: Get pre-synaptic information summary

get_pre_segment_id()¶

Get the ID of the pre-synpatic segment

Returns: ID of pre-synaptic segment.
Return type: str

ConnectionWD¶

class neuroml.nml.nml.ConnectionWD(neuro_lex_id=None, id=None, pre_cell_id=None, pre_segment_id='0', pre_fraction_along='0.5', post_cell_id=None, post_segment_id='0', post_fraction_along='0.5', weight=None, delay=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConnectionOldFormat

ConnectionWD – Event connection between named components, which gets processed via a new instance of a synapse component which is created on the target component, includes setting of weight and delay for the synaptic connection

Parameters

weight (none) –
delay (time) –

get_delay_in_ms()¶

Get connection delay in milli seconds

Returns: connection delay in milli seconds
Return type: float

get_post_cell_id()¶

Get the ID of the post-synaptic cell

Returns: ID of post-synaptic cell
Return type: str

get_post_fraction_along()¶: Get post-synaptic fraction along information

get_post_info()¶: Get post-synaptic information summary

get_post_segment_id()¶

Get the ID of the post-synpatic segment

Returns: ID of post-synaptic segment.
Return type: str

get_pre_cell_id()¶

Get the ID of the pre-synaptic cell

Returns: ID of pre-synaptic cell
Return type: str

get_pre_fraction_along()¶: Get pre-synaptic fraction along information

get_pre_info()¶: Get pre-synaptic information summary

get_pre_segment_id()¶

Get the ID of the pre-synpatic segment

Returns: ID of pre-synaptic segment.
Return type: str

Constant¶

class neuroml.nml.nml.Constant(name=None, dimension=None, value=None, description=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Constant – LEMS ComponentType for Constant.

ContinuousConnection¶

class neuroml.nml.nml.ContinuousConnection(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', pre_component=None, post_component=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConnectionNewFormat

ContinuousConnection – An instance of a connection in a continuousProjection between presynapticPopulation to another postsynapticPopulation through a preComponent at the start and postComponent at the end. Can be used for analog synapses.

get_post_cell_id()¶

Get the ID of the post-synaptic cell

Returns: ID of post-synaptic cell
Return type: str

get_post_fraction_along()¶: Get post-synaptic fraction along information

get_post_info()¶: Get post-synaptic information summary

get_post_segment_id()¶

Get the ID of the post-synpatic segment

Returns: ID of post-synaptic segment.
Return type: str

get_pre_cell_id()¶

Get the ID of the pre-synaptic cell

Returns: ID of pre-synaptic cell
Return type: str

get_pre_fraction_along()¶: Get pre-synaptic fraction along information

get_pre_info()¶: Get pre-synaptic information summary

get_pre_segment_id()¶

Get the ID of the pre-synpatic segment

Returns: ID of pre-synaptic segment.
Return type: str

ContinuousConnectionInstance¶

class neuroml.nml.nml.ContinuousConnectionInstance(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', pre_component=None, post_component=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ContinuousConnection

ContinuousConnectionInstance – An instance of a connection in a continuousProjection between presynapticPopulation to another postsynapticPopulation through a preComponent at the start and postComponent at the end. Populations need to be of type populationList and contain instance and location elements. Can be used for analog synapses.

ContinuousConnectionInstanceW¶

class neuroml.nml.nml.ContinuousConnectionInstanceW(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', pre_component=None, post_component=None, weight=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ContinuousConnectionInstance

ContinuousConnectionInstanceW – An instance of a connection in a continuousProjection between presynapticPopulation to another postsynapticPopulation through a preComponent at the start and postComponent at the end. Populations need to be of type populationList and contain instance and location elements. Can be used for analog synapses. Includes setting of weight for the connection

Parameters: weight (none) –

get_weight()¶

Get weight.

If weight is not set, the default value of 1.0 is returned.

ContinuousProjection¶

class neuroml.nml.nml.ContinuousProjection(neuro_lex_id=None, id=None, presynaptic_population=None, postsynaptic_population=None, continuous_connections=None, continuous_connection_instances=None, continuous_connection_instance_ws=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseProjection

ContinuousProjection – A projection between presynapticPopulation and postsynapticPopulation through components preComponent at the start and postComponent at the end of a continuousConnection or continuousConnectionInstance . Can be used for analog synapses.

exportHdf5(h5file, h5Group)¶: Export to HDF5 file.

DecayingPoolConcentrationModel¶

class neuroml.nml.nml.DecayingPoolConcentrationModel(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, ion=None, resting_conc=None, decay_constant=None, shell_thickness=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

DecayingPoolConcentrationModel – Model of an intracellular buffering mechanism for ion ( currently hard Coded to be calcium, due to requirement for iCa ) which has a baseline level restingConc and tends to this value with time course decayConstant. The ion is assumed to occupy a shell inside the membrane of thickness shellThickness.

Parameters

restingConc (concentration) –
decayConstant (time) –
shellThickness (length) –

DerivedVariable¶

class neuroml.nml.nml.DerivedVariable(name=None, dimension=None, description=None, exposure=None, value=None, select=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.NamedDimensionalVariable

DerivedVariable – LEMS ComponentType for DerivedVariable

DistalDetails¶

class neuroml.nml.nml.DistalDetails(normalization_end=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

DoubleSynapse¶

class neuroml.nml.nml.DoubleSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, synapse1=None, synapse2=None, synapse1_path=None, synapse2_path=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseVoltageDepSynapse

DoubleSynapse – Synapse consisting of two independent synaptic mechanisms ( e. g. AMPA-R and NMDA-R ), which can be easily colocated in connections

Dynamics¶

class neuroml.nml.nml.Dynamics(StateVariable=None, DerivedVariable=None, ConditionalDerivedVariable=None, TimeDerivative=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Dynamics – LEMS ComponentType for Dynamics

EIF_cond_alpha_isfa_ista¶

class neuroml.nml.nml.EIF_cond_alpha_isfa_ista(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, a=None, b=None, delta_T=None, tau_w=None, v_spike=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.EIF_cond_exp_isfa_ista

EIF_cond_alpha_isfa_ista – Adaptive exponential integrate and fire neuron according to Brette R and Gerstner W ( 2005 ) with alpha-function-shaped post-synaptic conductance

Parameters

v_spike (none) –
delta_T (none) –
tau_w (none) –
a (none) –
b (none) –
e_rev_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
e_rev_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

EIF_cond_exp_isfa_ista¶

class neuroml.nml.nml.EIF_cond_exp_isfa_ista(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, a=None, b=None, delta_T=None, tau_w=None, v_spike=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNIaFCondCell

EIF_cond_exp_isfa_ista – Adaptive exponential integrate and fire neuron according to Brette R and Gerstner W ( 2005 ) with exponentially-decaying post-synaptic conductance

Parameters

v_spike (none) –
delta_T (none) –
tau_w (none) –
a (none) –
b (none) –
e_rev_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
e_rev_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

ElectricalConnection¶

class neuroml.nml.nml.ElectricalConnection(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', synapse=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConnectionNewFormat

ElectricalConnection – To enable connections between populations through gap junctions.

get_post_cell_id()¶

Get the ID of the post-synaptic cell

Returns: ID of post-synaptic cell
Return type: str

get_post_fraction_along()¶: Get post-synaptic fraction along information

get_post_info()¶: Get post-synaptic information summary

get_post_segment_id()¶

Get the ID of the post-synpatic segment

Returns: ID of post-synaptic segment.
Return type: str

get_pre_cell_id()¶

Get the ID of the pre-synaptic cell

Returns: ID of pre-synaptic cell
Return type: str

get_pre_fraction_along()¶: Get pre-synaptic fraction along information

get_pre_info()¶: Get pre-synaptic information summary

get_pre_segment_id()¶

Get the ID of the pre-synpatic segment

Returns: ID of pre-synaptic segment.
Return type: str

ElectricalConnectionInstance¶

class neuroml.nml.nml.ElectricalConnectionInstance(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', synapse=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ElectricalConnection

ElectricalConnectionInstance – To enable connections between populations through gap junctions. Populations need to be of type populationList and contain instance and location elements.

ElectricalConnectionInstanceW¶

class neuroml.nml.nml.ElectricalConnectionInstanceW(neuro_lex_id=None, id=None, pre_cell=None, pre_segment='0', pre_fraction_along='0.5', post_cell=None, post_segment='0', post_fraction_along='0.5', synapse=None, weight=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.ElectricalConnectionInstance

ElectricalConnectionInstanceW – To enable connections between populations through gap junctions. Populations need to be of type populationList and contain instance and location elements. Includes setting of weight for the connection

Parameters: weight (none) –

get_weight()¶

Get the weight of the connection

If a weight is not set (or is set to None), returns the default value of 1.0.

Returns: weight of connection or 1.0 if not set
Return type: float

ElectricalProjection¶

class neuroml.nml.nml.ElectricalProjection(neuro_lex_id=None, id=None, presynaptic_population=None, postsynaptic_population=None, electrical_connections=None, electrical_connection_instances=None, electrical_connection_instance_ws=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseProjection

ElectricalProjection – A projection between presynapticPopulation to another postsynapticPopulation through gap junctions.

exportHdf5(h5file, h5Group)¶: Export to HDF5 file.

ExpCondSynapse¶

class neuroml.nml.nml.ExpCondSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, e_rev=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BasePynnSynapse

ExpCondSynapse – Conductance based synapse with instantaneous rise and single exponential decay ( with time constant tau_syn )

Parameters

e_rev (none) –
tau_syn (none) –

ExpCurrSynapse¶

class neuroml.nml.nml.ExpCurrSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BasePynnSynapse

ExpCurrSynapse – Current based synapse with instantaneous rise and single exponential decay ( with time constant tau_syn )

Parameters: tau_syn (none) –

ExpOneSynapse¶

class neuroml.nml.nml.ExpOneSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, tau_decay=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConductanceBasedSynapse

ExpOneSynapse – Ohmic synapse model whose conductance rises instantaneously by ( gbase * weight ) on receiving an event, and which decays exponentially to zero with time course tauDecay

Parameters

tauDecay (time) – Time course of decay
gbase (conductance) – Baseline conductance, generally the maximum conductance following a single spike
erev (voltage) – Reversal potential of the synapse

ExpThreeSynapse¶

class neuroml.nml.nml.ExpThreeSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase1=None, gbase2=None, erev=None, tau_decay1=None, tau_decay2=None, tau_rise=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConductanceBasedSynapseTwo

ExpThreeSynapse – Ohmic synapse similar to expTwoSynapse but consisting of two components that can differ in decay times and max conductances but share the same rise time.

Parameters

tauRise (time) –
tauDecay1 (time) –
tauDecay2 (time) –
gbase1 (conductance) – Baseline conductance 1
gbase2 (conductance) – Baseline conductance 2
erev (voltage) – Reversal potential of the synapse

ExpTwoSynapse¶

class neuroml.nml.nml.ExpTwoSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, tau_decay=None, tau_rise=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseConductanceBasedSynapse

ExpTwoSynapse – Ohmic synapse model whose conductance waveform on receiving an event has a rise time of tauRise and a decay time of tauDecay. Max conductance reached during this time ( assuming zero conductance before ) is gbase * weight.

Parameters

tauRise (time) –
tauDecay (time) –
gbase (conductance) – Baseline conductance, generally the maximum conductance following a single spike
erev (voltage) – Reversal potential of the synapse

ExplicitInput¶

class neuroml.nml.nml.ExplicitInput(target=None, input=None, destination=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

ExplicitInput – An explicit input ( anything which extends basePointCurrent ) to a target cell in a population

get_fraction_along()¶

Get fraction along.

Returns 0.5 is fraction_along was not set.

get_segment_id()¶

Get the ID of the segment.

Returns 0 if segment_id was not set.

get_target_cell_id()¶: Get target cell ID

get_target_population()¶: Get target population.

Exposure¶

class neuroml.nml.nml.Exposure(name=None, dimension=None, description=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Exposure – LEMS Exposure (ComponentType property)

ExtracellularProperties¶

class neuroml.nml.nml.ExtracellularProperties(neuro_lex_id=None, id=None, species=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.Base

ExtracellularPropertiesLocal¶

class neuroml.nml.nml.ExtracellularPropertiesLocal(species=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

FitzHughNagumo1969Cell¶

class neuroml.nml.nml.FitzHughNagumo1969Cell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, a=None, b=None, I=None, phi=None, V0=None, W0=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

FitzHughNagumo1969Cell – The Fitzhugh Nagumo model is a two-dimensional simplification of the Hodgkin-Huxley model of spike generation in squid giant axons. This system was suggested by FitzHugh ( FitzHugh R. [1961]: Impulses and physiological states in theoretical models of nerve membrane. Biophysical J. 1:445-466 ), who called it ” Bonhoeffer-van der Pol model “, and the equivalent circuit by Nagumo et al. ( Nagumo J. , Arimoto S. , and Yoshizawa S. [1962] An active pulse transmission line simulating nerve axon. Proc IRE. 50:2061-2070. 1962 ). This version corresponds to the one described in FitzHugh R. [1969]: Mathematical models of excitation and propagation in nerve. Chapter 1 ( pp. 1-85 in H. P. Schwan, ed. Biological Engineering, McGraw-Hill Book Co. , N. Y. )

Parameters

a (none) –
b (none) –
I (none) – plays the role of an external injected current
phi (none) –
V0 (none) –
W0 (none) –

FitzHughNagumoCell¶

class neuroml.nml.nml.FitzHughNagumoCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, I=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

FitzHughNagumoCell – Simple dimensionless model of spiking cell from FitzHugh and Nagumo. Superseded by fitzHughNagumo1969Cell ( See https://github.com/NeuroML/NeuroML2/issues/42 )

Parameters: I (none) –

FixedFactorConcentrationModel¶

class neuroml.nml.nml.FixedFactorConcentrationModel(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, ion=None, resting_conc=None, decay_constant=None, rho=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

FixedFactorConcentrationModel – Model of buffering of concentration of an ion ( currently hard coded to be calcium, due to requirement for iCa ) which has a baseline level restingConc and tends to this value with time course decayConstant. A fixed factor rho is used to scale the incoming current independently of the size of the compartment to produce a concentration change.

Parameters

restingConc (concentration) –
decayConstant (time) –
rho (rho_factor) –

ForwardTransition¶

class neuroml.nml.nml.ForwardTransition(neuro_lex_id=None, id=None, from_=None, to=None, anytypeobjs_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ForwardTransition – A forward only KSTransition for a gateKS which specifies a rate ( type baseHHRate ) which follows one of the standard Hodgkin Huxley forms ( e. g. HHExpRate , HHSigmoidRate , HHExpLinearRate

GapJunction¶

class neuroml.nml.nml.GapJunction(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, conductance=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

GapJunction – Gap junction/single electrical connection

Parameters: conductance (conductance) –

GateFractional¶

class neuroml.nml.nml.GateFractional(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, sub_gates=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateFractional – Gate composed of subgates contributing with fractional conductance

Parameters: instances (none) –

GateFractionalSubgate¶

class neuroml.nml.nml.GateFractionalSubgate(neuro_lex_id=None, id=None, fractional_conductance=None, notes=None, q10_settings=None, steady_state=None, time_course=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.Base

GateHHInstantaneous¶

class neuroml.nml.nml.GateHHInstantaneous(neuro_lex_id=None, id=None, instances=None, notes=None, steady_state=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHInstantaneous – Gate which follows the general Hodgkin Huxley formalism but is instantaneous, so tau = 0 and gate follows exactly inf value

Parameters: instances (none) –

GateHHRates¶

class neuroml.nml.nml.GateHHRates(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, forward_rate=None, reverse_rate=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHRates – Gate which follows the general Hodgkin Huxley formalism

Parameters: instances (none) –

GateHHRatesInf¶

class neuroml.nml.nml.GateHHRatesInf(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, forward_rate=None, reverse_rate=None, steady_state=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHRatesInf – Gate which follows the general Hodgkin Huxley formalism

Parameters: instances (none) –

GateHHRatesTau¶

class neuroml.nml.nml.GateHHRatesTau(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, forward_rate=None, reverse_rate=None, time_course=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHRatesTau – Gate which follows the general Hodgkin Huxley formalism

Parameters: instances (none) –

GateHHRatesTauInf¶

class neuroml.nml.nml.GateHHRatesTauInf(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, forward_rate=None, reverse_rate=None, time_course=None, steady_state=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHRatesTauInf – Gate which follows the general Hodgkin Huxley formalism

Parameters: instances (none) –

GateHHTauInf¶

class neuroml.nml.nml.GateHHTauInf(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, time_course=None, steady_state=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHTauInf – Gate which follows the general Hodgkin Huxley formalism

Parameters: instances (none) –

GateHHUndetermined¶

class neuroml.nml.nml.GateHHUndetermined(neuro_lex_id=None, id=None, instances=None, type=None, notes=None, q10_settings=None, forward_rate=None, reverse_rate=None, time_course=None, steady_state=None, sub_gates=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateHHUndetermined – Note all sub elements for gateHHrates, gateHHratesTau, gateFractional etc. allowed here. Which are valid should be constrained by what type is set

GateKS¶

class neuroml.nml.nml.GateKS(neuro_lex_id=None, id=None, instances=None, notes=None, q10_settings=None, closed_states=None, open_states=None, forward_transition=None, reverse_transition=None, tau_inf_transition=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

GateKS – A gate which consists of multiple KSState s and KSTransition s giving the rates of transition between them

Parameters: instances (none) –

GradedSynapse¶

class neuroml.nml.nml.GradedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, conductance=None, delta=None, Vth=None, k=None, erev=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

GradedSynapse – Graded/analog synapse. Based on synapse in Methods of http://www. nature.com/neuro/journal/v7/n12/abs/nn1352.html

Parameters

conductance (conductance) –
delta (voltage) – Slope of the activation curve
k (per_time) – Rate constant for transmitter-receptor dissociation rate
Vth (voltage) – The half-activation voltage of the synapse
erev (voltage) – The reversal potential of the synapse

GridLayout¶

class neuroml.nml.nml.GridLayout(x_size=None, y_size=None, z_size=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

HHRate¶

class neuroml.nml.nml.HHRate(type=None, rate=None, midpoint=None, scale=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

HHTime¶

class neuroml.nml.nml.HHTime(type=None, rate=None, midpoint=None, scale=None, tau=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

HHVariable¶

class neuroml.nml.nml.HHVariable(type=None, rate=None, midpoint=None, scale=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

HH_cond_exp¶

class neuroml.nml.nml.HH_cond_exp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, v_offset=None, e_rev_E=None, e_rev_I=None, e_rev_K=None, e_rev_Na=None, e_rev_leak=None, g_leak=None, gbar_K=None, gbar_Na=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNCell

HH_cond_exp – Single-compartment Hodgkin-Huxley-type neuron with transient sodium and delayed-rectifier potassium currents using the ion channel models from Traub.

Parameters

gbar_K (none) –
gbar_Na (none) –
g_leak (none) –
e_rev_K (none) –
e_rev_Na (none) –
e_rev_leak (none) –
v_offset (none) –
e_rev_E (none) –
e_rev_I (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell

:type tau _syn_I: none :param v_init: :type v_init: none

IF_cond_alpha¶

class neuroml.nml.nml.IF_cond_alpha(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNIaFCondCell

IF_cond_alpha – Leaky integrate and fire model with fixed threshold and alpha-function-shaped post-synaptic conductance

Parameters

e_rev_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
e_rev_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

IF_cond_exp¶

class neuroml.nml.nml.IF_cond_exp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNIaFCondCell

IF_cond_exp – Leaky integrate and fire model with fixed threshold and exponentially-decaying post-synaptic conductance

Parameters

e_rev_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
e_rev_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

IF_curr_alpha¶

class neuroml.nml.nml.IF_curr_alpha(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNIaFCell

IF_curr_alpha – Leaky integrate and fire model with fixed threshold and alpha-function-shaped post-synaptic current

Parameters

tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

IF_curr_exp¶

class neuroml.nml.nml.IF_curr_exp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNIaFCell

IF_curr_exp – Leaky integrate and fire model with fixed threshold and decaying-exponential post-synaptic current

Parameters

tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

IafCell¶

class neuroml.nml.nml.IafCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, leak_reversal=None, thresh=None, reset=None, C=None, leak_conductance=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

IafCell – Integrate and fire cell with capacitance C, leakConductance and leakReversal

Parameters

leakConductance (conductance) –
leakReversal (voltage) –
thresh (voltage) –
reset (voltage) –
C (capacitance) – Total capacitance of the cell membrane

IafRefCell¶

class neuroml.nml.nml.IafRefCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, leak_reversal=None, thresh=None, reset=None, C=None, leak_conductance=None, refract=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.IafCell

IafRefCell – Integrate and fire cell with capacitance C, leakConductance, leakReversal and refractory period refract

Parameters

refract (time) –
leakConductance (conductance) –
leakReversal (voltage) –
thresh (voltage) –
reset (voltage) –
C (capacitance) – Total capacitance of the cell membrane

IafTauCell¶

class neuroml.nml.nml.IafTauCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, leak_reversal=None, thresh=None, reset=None, tau=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

IafTauCell – Integrate and fire cell which returns to its leak reversal potential of leakReversal with a time constant tau

Parameters

leakReversal (voltage) –
tau (time) –
thresh (voltage) – The membrane potential at which to emit a spiking event and reset voltage
reset (voltage) – The value the membrane potential is reset to on spiking

IafTauRefCell¶

class neuroml.nml.nml.IafTauRefCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, leak_reversal=None, thresh=None, reset=None, tau=None, refract=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.IafTauCell

IafTauRefCell – Integrate and fire cell which returns to its leak reversal potential of leakReversal with a time course tau. It has a refractory period of refract after spiking

Parameters

refract (time) –
leakReversal (voltage) –
tau (time) –
thresh (voltage) – The membrane potential at which to emit a spiking event and reset voltage
reset (voltage) – The value the membrane potential is reset to on spiking

Include¶

class neuroml.nml.nml.Include(segment_groups=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Include – Include all members of another segmentGroup in this group

IncludeType¶

class neuroml.nml.nml.IncludeType(href=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

InhomogeneousParameter¶

class neuroml.nml.nml.InhomogeneousParameter(neuro_lex_id=None, id=None, variable=None, metric=None, proximal=None, distal=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

InhomogeneousParameter – An inhomogeneous parameter specified across the segmentGroup ( see variableParameter for usage ).

InhomogeneousValue¶

class neuroml.nml.nml.InhomogeneousValue(inhomogeneous_parameters=None, value=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

InhomogeneousValue – Specifies the value of an inhomogeneousParameter. For usage see variableParameter

InitMembPotential¶

class neuroml.nml.nml.InitMembPotential(value=None, segment_groups='all', gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

InitMembPotential – Explicitly set initial membrane potential for the cell

Parameters: value (voltage) –

Input¶

class neuroml.nml.nml.Input(id=None, target=None, destination=None, segment_id=None, fraction_along=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Input – Specifies a single input to a target, optionally giving the segmentId ( default 0 ) and fractionAlong the segment ( default 0. 5 ).

get_fraction_along()¶

Get fraction along.

Returns 0.5 is fraction_along was not set.

get_segment_id()¶

Get the ID of the segment.

Returns 0 if segment_id was not set.

get_target_cell_id()¶: Get ID of target cell.

InputList¶

class neuroml.nml.nml.InputList(neuro_lex_id=None, id=None, populations=None, component=None, input=None, input_ws=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

InputList – An explicit list of input s to a population.

exportHdf5(h5file, h5Group)¶: Export to HDF5 file.

InputW¶

class neuroml.nml.nml.InputW(id=None, target=None, destination=None, segment_id=None, fraction_along=None, weight=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Input

InputW – Specifies input lists. Can set weight to scale individual inputs.

Parameters: weight (none) –

get_weight()¶

Get weight.

If weight is not set, the default value of 1.0 is returned.

Instance¶

class neuroml.nml.nml.Instance(id=None, i=None, j=None, k=None, location=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Instance – Specifies a single instance of a component in a population ( placed at location ).

InstanceRequirement¶

class neuroml.nml.nml.InstanceRequirement(name=None, type=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

IntracellularProperties¶

class neuroml.nml.nml.IntracellularProperties(species=None, resistivities=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

IntracellularProperties – Biophysical properties related to the intracellular space within the cell , such as the resistivity and the list of ionic species present. caConc and caConcExt are explicitly exposed here to facilitate accessing these values from other Components, even though caConcExt is clearly not an intracellular property

IntracellularProperties2CaPools¶

class neuroml.nml.nml.IntracellularProperties2CaPools(species=None, resistivities=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.IntracellularProperties

IntracellularProperties2CaPools – Variant of intracellularProperties with 2 independent Ca pools

IonChannel¶

class neuroml.nml.nml.IonChannel(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, q10_conductance_scalings=None, species=None, type=None, conductance=None, gates=None, gate_hh_rates=None, gate_h_hrates_taus=None, gate_hh_tau_infs=None, gate_h_hrates_infs=None, gate_h_hrates_tau_infs=None, gate_hh_instantaneouses=None, gate_fractionals=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.IonChannelScalable

IonChannel – Note ionChannel and ionChannelHH are currently functionally identical. This is needed since many existing examples use ionChannel, some use ionChannelHH. NeuroML v2beta4 should remove one of these, probably ionChannelHH.

Parameters: conductance (conductance) –

IonChannelHH¶

class neuroml.nml.nml.IonChannelHH(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, q10_conductance_scalings=None, species=None, type=None, conductance=None, gates=None, gate_hh_rates=None, gate_h_hrates_taus=None, gate_hh_tau_infs=None, gate_h_hrates_infs=None, gate_h_hrates_tau_infs=None, gate_hh_instantaneouses=None, gate_fractionals=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.IonChannel

IonChannelHH – Note ionChannel and ionChannelHH are currently functionally identical. This is needed since many existing examples use ionChannel, some use ionChannelHH. NeuroML v2beta4 should remove one of these, probably ionChannelHH.

Parameters: conductance (conductance) –

IonChannelKS¶

class neuroml.nml.nml.IonChannelKS(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, species=None, conductance=None, gate_kses=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

A kinetic scheme based ion channel with multiple gateKS s, each of which consists of multiple KSState s and KSTransition s giving the rates of transition between them IonChannelKS – A kinetic scheme based ion channel with multiple gateKS s, each of which consists of multiple KSState s and KSTransition s giving the rates of transition between them

Parameters: conductance (conductance) –

IonChannelScalable¶

class neuroml.nml.nml.IonChannelScalable(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, q10_conductance_scalings=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.Standalone

IonChannelVShift¶

class neuroml.nml.nml.IonChannelVShift(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, q10_conductance_scalings=None, species=None, type=None, conductance=None, gates=None, gate_hh_rates=None, gate_h_hrates_taus=None, gate_hh_tau_infs=None, gate_h_hrates_infs=None, gate_h_hrates_tau_infs=None, gate_hh_instantaneouses=None, gate_fractionals=None, v_shift=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.IonChannel

IonChannelVShift – Same as ionChannel , but with a vShift parameter to change voltage activation of gates. The exact usage of vShift in expressions for rates is determined by the individual gates.

Parameters

vShift (voltage) –
conductance (conductance) –

Izhikevich2007Cell¶

class neuroml.nml.nml.Izhikevich2007Cell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, C=None, v0=None, k=None, vr=None, vt=None, vpeak=None, a=None, b=None, c=None, d=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCellMembPotCap

Izhikevich2007Cell – Cell based on the modified Izhikevich model in Izhikevich 2007, Dynamical systems in neuroscience, MIT Press

Parameters

v0 (voltage) –
k (conductance_per_voltage) –
vr (voltage) –
vt (voltage) –
vpeak (voltage) –
a (per_time) –
b (conductance) –
c (voltage) –
d (current) –
C (capacitance) – Total capacitance of the cell membrane

IzhikevichCell¶

class neuroml.nml.nml.IzhikevichCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, v0=None, thresh=None, a=None, b=None, c=None, d=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

IzhikevichCell – Cell based on the 2003 model of Izhikevich, see http://izhikevich.org/publications/spikes.htm

Parameters

v0 (voltage) – Initial membrane potential
a (none) – Time scale of the recovery variable U
b (none) – Sensitivity of U to the subthreshold fluctuations of the membrane potential V
c (none) – After-spike reset value of V
d (none) – After-spike increase to U
thresh (voltage) – Spike threshold

LEMS_Property¶

class neuroml.nml.nml.LEMS_Property(name=None, dimension=None, description=None, default_value=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.NamedDimensionalType

Layout¶

class neuroml.nml.nml.Layout(spaces=None, random=None, grid=None, unstructured=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

LinearGradedSynapse¶

class neuroml.nml.nml.LinearGradedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, conductance=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

LinearGradedSynapse – Behaves just like a one way gap junction.

Parameters: conductance (conductance) –

Location¶

class neuroml.nml.nml.Location(x=None, y=None, z=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Location – Specifies the ( x, y, z ) location of a single instance of a component in a population

Parameters

x (none) –
y (none) –
z (none) –

Member¶

class neuroml.nml.nml.Member(segments=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Member – A single identified segment which is part of the segmentGroup

MembraneProperties¶

class neuroml.nml.nml.MembraneProperties(channel_populations=None, channel_densities=None, channel_density_v_shifts=None, channel_density_nernsts=None, channel_density_ghks=None, channel_density_ghk2s=None, channel_density_non_uniforms=None, channel_density_non_uniform_nernsts=None, channel_density_non_uniform_ghks=None, spike_threshes=None, specific_capacitances=None, init_memb_potentials=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

MembraneProperties – Properties specific to the membrane, such as the populations of channels, channelDensities, specificCapacitance, etc.

MembraneProperties2CaPools¶

class neuroml.nml.nml.MembraneProperties2CaPools(channel_populations=None, channel_densities=None, channel_density_v_shifts=None, channel_density_nernsts=None, channel_density_ghks=None, channel_density_ghk2s=None, channel_density_non_uniforms=None, channel_density_non_uniform_nernsts=None, channel_density_non_uniform_ghks=None, spike_threshes=None, specific_capacitances=None, init_memb_potentials=None, channel_density_nernst_ca2s=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.MembraneProperties

MembraneProperties2CaPools – Variant of membraneProperties with 2 independent Ca pools

MixedContainer:

Morphology¶

class neuroml.nml.nml.Morphology(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, segments=None, segment_groups=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

Morphology – The collection of segment s which specify the 3D structure of the cell, along with a number of segmentGroup s

property num_segments¶

Get the number of segments included in this cell morphology.

Returns: number of segments
Return type: int

NamedDimensionalType¶

class neuroml.nml.nml.NamedDimensionalType(name=None, dimension=None, description=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

NamedDimensionalVariable¶

class neuroml.nml.nml.NamedDimensionalVariable(name=None, dimension=None, description=None, exposure=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

Network¶

class neuroml.nml.nml.Network(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, type=None, temperature=None, spaces=None, regions=None, extracellular_properties=None, populations=None, cell_sets=None, synaptic_connections=None, projections=None, electrical_projections=None, continuous_projections=None, explicit_inputs=None, input_lists=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

Network – Network containing: population s ( potentially of type populationList , and so specifying a list of cell location s ); projection s ( with lists of connection s ) and/or explicitConnection s; and inputList s ( with lists of input s ) and/or explicitInput s. Note: often in NeuroML this will be of type networkWithTemperature if there are temperature dependent elements ( e. g. ion channels ).

exportHdf5(h5file, h5Group)¶: Export to HDF5 file.

get_by_id(id)¶

Get a component by its ID

Parameters: id (str) – ID of component to find
Returns: component with specified ID or None if no component with specified ID found

NeuroMLDocument¶

class neuroml.nml.nml.NeuroMLDocument(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, includes=None, extracellular_properties=None, intracellular_properties=None, morphology=None, ion_channel=None, ion_channel_hhs=None, ion_channel_v_shifts=None, ion_channel_kses=None, decaying_pool_concentration_models=None, fixed_factor_concentration_models=None, alpha_current_synapses=None, alpha_synapses=None, exp_one_synapses=None, exp_two_synapses=None, exp_three_synapses=None, blocking_plastic_synapses=None, double_synapses=None, gap_junctions=None, silent_synapses=None, linear_graded_synapses=None, graded_synapses=None, biophysical_properties=None, cells=None, cell2_ca_poolses=None, base_cells=None, iaf_tau_cells=None, iaf_tau_ref_cells=None, iaf_cells=None, iaf_ref_cells=None, izhikevich_cells=None, izhikevich2007_cells=None, ad_ex_ia_f_cells=None, fitz_hugh_nagumo_cells=None, fitz_hugh_nagumo1969_cells=None, pinsky_rinzel_ca3_cells=None, pulse_generators=None, pulse_generator_dls=None, sine_generators=None, sine_generator_dls=None, ramp_generators=None, ramp_generator_dls=None, compound_inputs=None, compound_input_dls=None, voltage_clamps=None, voltage_clamp_triples=None, spike_arrays=None, timed_synaptic_inputs=None, spike_generators=None, spike_generator_randoms=None, spike_generator_poissons=None, spike_generator_ref_poissons=None, poisson_firing_synapses=None, transient_poisson_firing_synapses=None, IF_curr_alpha=None, IF_curr_exp=None, IF_cond_alpha=None, IF_cond_exp=None, EIF_cond_exp_isfa_ista=None, EIF_cond_alpha_isfa_ista=None, HH_cond_exp=None, exp_cond_synapses=None, alpha_cond_synapses=None, exp_curr_synapses=None, alpha_curr_synapses=None, SpikeSourcePoisson=None, networks=None, ComponentType=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

append(element)¶

Append an element

Parameters: element (Object) – element to append

get_by_id(id)¶

Get a component by specifying its ID.

Parameters: id (str) – id of Component to get
Returns: Component with given ID or None if no Component with provided ID was found

summary(show_includes=True, show_non_network=True)¶

Get a pretty-printed summary of the complete NeuroMLDocument.

This includes information on the various Components included in the NeuroMLDocument: networks, cells, projections, synapses, and so on.

OpenState¶

class neuroml.nml.nml.OpenState(neuro_lex_id=None, id=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

OpenState – A KSState with relativeConductance of 1

Parameters: relativeConductance (none) –

Parameter¶

class neuroml.nml.nml.Parameter(name=None, dimension=None, description=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.NamedDimensionalType

Path¶

class neuroml.nml.nml.Path(from_=None, to=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Path – Include all the segment s between those specified by from and to , inclusive

PinskyRinzelCA3Cell¶

class neuroml.nml.nml.PinskyRinzelCA3Cell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, i_soma=None, i_dend=None, gc=None, g_ls=None, g_ld=None, g_na=None, g_kdr=None, g_ca=None, g_kahp=None, g_kc=None, g_nmda=None, g_ampa=None, e_na=None, e_ca=None, e_k=None, e_l=None, qd0=None, pp=None, alphac=None, betac=None, cm=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

PinskyRinzelCA3Cell – Reduced CA3 cell model from Pinsky and Rinzel 1994. See https://github.com/OpenSourceBrain/PinskyRinzelModel

Parameters

iSoma (currentDensity) –
iDend (currentDensity) –
gLs (conductanceDensity) –
gLd (conductanceDensity) –
gNa (conductanceDensity) –
gKdr (conductanceDensity) –
gCa (conductanceDensity) –
gKahp (conductanceDensity) –
gKC (conductanceDensity) –
gc (conductanceDensity) –
eNa (voltage) –
eCa (voltage) –
eK (voltage) –
eL (voltage) –
pp (none) –
cm (specificCapacitance) –
alphac (none) –
betac (none) –
gNmda (conductanceDensity) –
gAmpa (conductanceDensity) –
qd0 (none) –

PlasticityMechanism¶

class neuroml.nml.nml.PlasticityMechanism(type=None, init_release_prob=None, tau_rec=None, tau_fac=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

Point3DWithDiam¶

class neuroml.nml.nml.Point3DWithDiam(x=None, y=None, z=None, diameter=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Point3DWithDiam – Base type for ComponentTypes which specify an ( x, y, z ) coordinate along with a diameter. Note: no dimension used in the attributes for these coordinates! These are assumed to have dimension micrometer ( 10^-6 m ). This is due to micrometers being the default option for the majority of neuronal morphology formats, and dimensions are omitted here to facilitate reading and writing of morphologies in NeuroML.

Parameters

x (none) – x coordinate of the point. Note: no dimension used, see description of point3DWithDiam for details.
y (none) – y coordinate of the ppoint. Note: no dimension used, see description of point3DWithDiam for details.
z (none) – z coordinate of the ppoint. Note: no dimension used, see description of point3DWithDiam for details.
diameter (none) – Diameter of the ppoint. Note: no dimension used, see description of point3DWithDiam for details.

distance_to(other_3d_point)¶

Find the distance between this point and another.

Parameters: other_3d_point (Point3DWithDiam) – other 3D point to calculate distance to
Returns: distance between the two points
Return type: float

PoissonFiringSynapse¶

class neuroml.nml.nml.PoissonFiringSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, average_rate=None, synapse=None, spike_target=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

PoissonFiringSynapse – Poisson spike generator firing at averageRate, which is connected to single synapse that is triggered every time a spike is generated, producing an input current. See also transientPoissonFiringSynapse .

Parameters: averageRate (per_time) – The average rate at which spikes are emitted

Population¶

class neuroml.nml.nml.Population(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, component=None, size=None, type=None, extracellular_properties=None, layout=None, instances=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

Population – A population of components, with just one parameter for the size, i. e. number of components to create. Note: quite often this is used with type= populationList which means the size is determined by the number of instance s ( with location s ) in the list. The size attribute is still set, and there will be a validation error if this does not match the number in the list.

Parameters: size (none) – Number of instances of this Component to create when the population is instantiated

exportHdf5(h5file, h5Group)¶: Export to HDF5 file.

get_size()¶

Projection¶

class neuroml.nml.nml.Projection(neuro_lex_id=None, id=None, presynaptic_population=None, postsynaptic_population=None, synapse=None, connections=None, connection_wds=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseProjection

Projection – Projection from one population, presynapticPopulation to another, postsynapticPopulation, through synapse. Contains lists of connection or connectionWD elements.

exportHdf5(h5file, h5Group)¶: Export to HDF5 file.

Property¶

class neuroml.nml.nml.Property(tag=None, value=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Property – A property ( a tag and value pair ), which can be on any baseStandalone either as a direct child, or within an Annotation . Generally something which helps the visual display or facilitates simulation of a Component, but is not a core physiological property. Common examples include: numberInternalDivisions, equivalent of nseg in NEURON; radius, for a radius to use in graphical displays for abstract cells ( i. e. without defined morphologies ); color, the color to use for a Population or populationList of cells; recommended_dt_ms, the recommended timestep to use for simulating a Network , recommended_duration_ms the recommended duration to use when running a Network

ProximalDetails¶

class neuroml.nml.nml.ProximalDetails(translation_start=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

PulseGenerator¶

class neuroml.nml.nml.PulseGenerator(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, duration=None, amplitude=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

PulseGenerator – Generates a constant current pulse of a certain amplitude for a specified duration after a delay. Scaled by weight, if set

Parameters

delay (time) – Delay before change in current. Current is zero prior to this.
duration (time) – Duration for holding current at amplitude. Current is zero after delay + duration.
amplitude (current) – Amplitude of current pulse

PulseGeneratorDL¶

class neuroml.nml.nml.PulseGeneratorDL(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, duration=None, amplitude=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

PulseGeneratorDL – Dimensionless equivalent of pulseGenerator . Generates a constant current pulse of a certain amplitude for a specified duration after a delay. Scaled by weight, if set

Parameters

delay (time) – Delay before change in current. Current is zero prior to this.
duration (time) – Duration for holding current at amplitude. Current is zero after delay + duration.
amplitude (none) – Amplitude of current pulse

Q10ConductanceScaling¶

class neuroml.nml.nml.Q10ConductanceScaling(q10_factor=None, experimental_temp=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Q10ConductanceScaling – A value for the conductance scaling which varies as a standard function of the difference between the current temperature, temperature, and the temperature at which the conductance was originally determined, experimentalTemp

Parameters

q10Factor (none) –
experimentalTemp (temperature) –

Q10Settings¶

class neuroml.nml.nml.Q10Settings(type=None, fixed_q10=None, q10_factor=None, experimental_temp=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

RampGenerator¶

class neuroml.nml.nml.RampGenerator(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, duration=None, start_amplitude=None, finish_amplitude=None, baseline_amplitude=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

RampGenerator – Generates a ramping current after a time delay, for a fixed duration. During this time the current steadily changes from startAmplitude to finishAmplitude. Scaled by weight, if set

Parameters

delay (time) – Delay before change in current. Current is baselineAmplitude prior to this.
duration (time) – Duration for holding current at amplitude. Current is baselineAmplitude after delay + duration.
startAmplitude (current) – Amplitude of linearly varying current at time delay
finishAmplitude (current) – Amplitude of linearly varying current at time delay + duration
baselineAmplitude (current) – Amplitude of current before time delay, and after time delay + duration

RampGeneratorDL¶

class neuroml.nml.nml.RampGeneratorDL(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, duration=None, start_amplitude=None, finish_amplitude=None, baseline_amplitude=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

RampGeneratorDL – Dimensionless equivalent of rampGenerator . Generates a ramping current after a time delay, for a fixed duration. During this time the dimensionless current steadily changes from startAmplitude to finishAmplitude. Scaled by weight, if set

Parameters

delay (time) – Delay before change in current. Current is baselineAmplitude prior to this.
duration (time) – Duration for holding current at amplitude. Current is baselineAmplitude after delay + duration.
startAmplitude (none) – Amplitude of linearly varying current at time delay
finishAmplitude (none) – Amplitude of linearly varying current at time delay + duration
baselineAmplitude (none) – Amplitude of current before time delay, and after time delay + duration

RandomLayout¶

class neuroml.nml.nml.RandomLayout(number=None, regions=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

ReactionScheme¶

class neuroml.nml.nml.ReactionScheme(neuro_lex_id=None, id=None, source=None, type=None, anytypeobjs_=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.Base

Region¶

class neuroml.nml.nml.Region(neuro_lex_id=None, id=None, spaces=None, anytypeobjs_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

Region – Initial attempt to specify 3D region for placing cells. Work in progress…

Requirement¶

class neuroml.nml.nml.Requirement(name=None, dimension=None, description=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.NamedDimensionalType

Resistivity¶

class neuroml.nml.nml.Resistivity(value=None, segment_groups='all', gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Resistivity – The resistivity, or specific axial resistance, of the cytoplasm

Parameters: value (resistivity) –

validate_Nml2Quantity_resistivity(value)¶

validate_Nml2Quantity_resistivity_patterns_ = [['^(-?([0-9]*(\\.[0-9]+)?)([eE]-?[0-9]+)?[\\s]*(ohm_cm|kohm_cm|ohm_m))$']]¶

ReverseTransition¶

class neuroml.nml.nml.ReverseTransition(neuro_lex_id=None, id=None, from_=None, to=None, anytypeobjs_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

ReverseTransition – A reverse only KSTransition for a gateKS which specifies a rate ( type baseHHRate ) which follows one of the standard Hodgkin Huxley forms ( e. g. HHExpRate , HHSigmoidRate , HHExpLinearRate

Segment¶

class neuroml.nml.nml.Segment(neuro_lex_id=None, id=None, name=None, parent=None, proximal=None, distal=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseNonNegativeIntegerId

Segment – A segment defines the smallest unit within a possibly branching structure ( morphology ), such as a dendrite or axon. Its id should be a nonnegative integer ( usually soma/root = 0 ). Its end points are given by the proximal and distal points. The proximal point can be omitted, usually because it is the same as a point on the parent segment, see proximal for details. parent specifies the parent segment. The first segment of a cell ( with no parent ) usually represents the soma. The shape is normally a cylinder ( radii of the proximal and distal equal, but positions different ) or a conical frustum ( radii and positions different ). If the x, y, x positions of the proximal and distal are equal, the segment can be interpreted as a sphere, and in this case the radii of these points must be equal. NOTE: LEMS does not yet support multicompartmental modelling, so the Dynamics here is only appropriate for single compartment modelling.

property length¶

Get the length of the segment.

Returns: length of the segment
Return type: float

property surface_area¶

Get the surface area of the segment.

Returns: surface area of segment
Return type: float

property volume¶

Get the volume of the segment.

Returns: volume of segment
Return type: float

SegmentEndPoint¶

class neuroml.nml.nml.SegmentEndPoint(segments=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

SegmentGroup¶

class neuroml.nml.nml.SegmentGroup(neuro_lex_id=None, id=None, notes=None, properties=None, annotation=None, members=None, includes=None, paths=None, sub_trees=None, inhomogeneous_parameters=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

SegmentGroup – A method to describe a group of segment s in a morphology , e. g. soma_group, dendrite_group, axon_group. While a name is useful to describe the group, the neuroLexId attribute can be used to explicitly specify the meaning of the group, e. g. sao1044911821 for ‘Neuronal Cell Body’, sao1211023249 for ‘Dendrite’. The segment s in this group can be specified as: a list of individual member segments; a path , all of the segments along which should be included; a subTree of the cell to include; other segmentGroups to include ( so all segments from those get included here ). An inhomogeneousParameter can be defined on the region of the cell specified by this group ( see variableParameter for usage ).

SegmentParent¶

class neuroml.nml.nml.SegmentParent(segments=None, fraction_along='1', gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

SilentSynapse¶

class neuroml.nml.nml.SilentSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseSynapse

SilentSynapse – Dummy synapse which emits no current. Used as presynaptic endpoint for analog synaptic connection.

SineGenerator¶

class neuroml.nml.nml.SineGenerator(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, phase=None, duration=None, amplitude=None, period=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SineGenerator – Generates a sinusoidally varying current after a time delay, for a fixed duration. The period and maximum amplitude of the current can be set as well as the phase at which to start. Scaled by weight, if set

Parameters

phase (none) – Phase ( between 0 and 2*pi ) at which to start the varying current ( i. e. at time given by delay )
delay (time) – Delay before change in current. Current is zero prior to this.
duration (time) – Duration for holding current at amplitude. Current is zero after delay + duration.
amplitude (current) – Maximum amplitude of current
period (time) – Time period of oscillation

SineGeneratorDL¶

class neuroml.nml.nml.SineGeneratorDL(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, phase=None, duration=None, amplitude=None, period=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SineGeneratorDL – Dimensionless equivalent of sineGenerator . Generates a sinusoidally varying current after a time delay, for a fixed duration. The period and maximum amplitude of the current can be set as well as the phase at which to start. Scaled by weight, if set

Parameters

phase (none) – Phase ( between 0 and 2*pi ) at which to start the varying current ( i. e. at time given by delay )
delay (time) – Delay before change in current. Current is zero prior to this.
duration (time) – Duration for holding current at amplitude. Current is zero after delay + duration.
amplitude (none) – Maximum amplitude of current
period (time) – Time period of oscillation

Space¶

class neuroml.nml.nml.Space(neuro_lex_id=None, id=None, based_on=None, structure=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.Base

SpaceStructure¶

class neuroml.nml.nml.SpaceStructure(x_spacing=None, y_spacing=None, z_spacing=None, x_start=0, y_start=0, z_start=0, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

Species¶

class neuroml.nml.nml.Species(id=None, concentration_model=None, ion=None, initial_concentration=None, initial_ext_concentration=None, segment_groups='all', gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

Species – Description of a chemical species identified by ion, which has internal, concentration, and external, extConcentration values for its concentration

:param initialConcentration : :type initialConcentration: concentration :param initialExtConcentration: :type initialExtConcentration: concentration

SpecificCapacitance¶

class neuroml.nml.nml.SpecificCapacitance(value=None, segment_groups='all', gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

SpecificCapacitance – Capacitance per unit area

Parameters: value (specificCapacitance) –

Spike¶

class neuroml.nml.nml.Spike(neuro_lex_id=None, id=None, time=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseNonNegativeIntegerId

Spike – Emits a single spike at the specified time

Parameters: time (time) – Time at which to emit one spike event

SpikeArray¶

class neuroml.nml.nml.SpikeArray(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, spikes=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SpikeArray – Set of spike ComponentTypes, each emitting one spike at a certain time. Can be used to feed a predetermined spike train into a cell

SpikeGenerator¶

class neuroml.nml.nml.SpikeGenerator(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, period=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SpikeGenerator – Simple generator of spikes at a regular interval set by period

Parameters: period (time) – Time between spikes. The first spike will be emitted after this time.

SpikeGeneratorPoisson¶

class neuroml.nml.nml.SpikeGeneratorPoisson(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, average_rate=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SpikeGeneratorPoisson – Generator of spikes whose ISI is distributed according to an exponential PDF with scale: 1 / averageRate

Parameters: averageRate (per_time) – The average rate at which spikes are emitted

SpikeGeneratorRandom¶

class neuroml.nml.nml.SpikeGeneratorRandom(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, max_isi=None, min_isi=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SpikeGeneratorRandom – Generator of spikes with a random interspike interval of at least minISI and at most maxISI

Parameters

maxISI (time) – Maximum interspike interval
minISI (time) – Minimum interspike interval

SpikeGeneratorRefPoisson¶

class neuroml.nml.nml.SpikeGeneratorRefPoisson(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, average_rate=None, minimum_isi=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.SpikeGeneratorPoisson

SpikeGeneratorRefPoisson – Generator of spikes whose ISI distribution is the maximum entropy distribution over [ minimumISI, +infinity ) with mean: 1 / averageRate

Parameters

minimumISI (time) – The minimum interspike interval
averageRate (per_time) – The average rate at which spikes are emitted

SpikeSourcePoisson¶

class neuroml.nml.nml.SpikeSourcePoisson(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, start=None, duration=None, rate=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

SpikeSourcePoisson – Spike source, generating spikes according to a Poisson process.

Parameters

start (time) –
duration (time) –
rate (per_time) –

SpikeThresh¶

class neuroml.nml.nml.SpikeThresh(value=None, segment_groups='all', gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

SpikeThresh – Membrane potential at which to emit a spiking event. Note, usually the spiking event will not be emitted again until the membrane potential has fallen below this value and rises again to cross it in a positive direction

Parameters: value (voltage) –

Standalone¶

class neuroml.nml.nml.Standalone(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

Standalone – Elements which can stand alone and be referenced by id, e.g. cell, morphology.

StateVariable¶

class neuroml.nml.nml.StateVariable(name=None, dimension=None, description=None, exposure=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.NamedDimensionalVariable

SubTree¶

class neuroml.nml.nml.SubTree(from_=None, to=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

SubTree – Include all the segment s distal to that specified by from in the segmentGroup

SynapticConnection¶

class neuroml.nml.nml.SynapticConnection(from_=None, to=None, synapse=None, destination=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

SynapticConnection – Explicit event connection between named components, which gets processed via a new instance of a synapse component which is created on the target component

TauInfTransition¶

class neuroml.nml.nml.TauInfTransition(neuro_lex_id=None, id=None, from_=None, to=None, steady_state=None, time_course=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Base

TauInfTransition – KS Transition specified in terms of time constant tau and steady state inf

TimeDerivative¶

class neuroml.nml.nml.TimeDerivative(variable=None, value=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

TimedSynapticInput¶

class neuroml.nml.nml.TimedSynapticInput(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, synapse=None, spike_target=None, spikes=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

TimedSynapticInput – Spike array connected to a single synapse, producing a current triggered by each spike in the array.

TransientPoissonFiringSynapse¶

class neuroml.nml.nml.TransientPoissonFiringSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, average_rate=None, delay=None, duration=None, synapse=None, spike_target=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

TransientPoissonFiringSynapse – Poisson spike generator firing at averageRate after a delay and for a duration, connected to single synapse that is triggered every time a spike is generated, providing an input current. Similar to ComponentType poissonFiringSynapse .

Parameters

averageRate (per_time) –
delay (time) –
duration (time) –

UnstructuredLayout¶

class neuroml.nml.nml.UnstructuredLayout(number=None, gds_collector_=None, **kwargs_)¶: Bases: neuroml.nml.nml.GeneratedsSuper

VariableParameter¶

class neuroml.nml.nml.VariableParameter(parameter=None, segment_groups=None, inhomogeneous_value=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.GeneratedsSuper

VariableParameter – Specifies a parameter ( e. g. condDensity ) which can vary its value across a segmentGroup. The value is calculated from value attribute of the inhomogeneousValue subelement. This element is normally a child of channelDensityNonUniform , channelDensityNonUniformNernst or channelDensityNonUniformGHK and is used to calculate the value of the conductance, etc. which will vary on different parts of the cell. The segmentGroup specified here needs to define an inhomogeneousParameter ( referenced from inhomogeneousParameter in the inhomogeneousValue ), which calculates a variable ( e. g. p ) varying across the cell ( e. g. based on the path length from soma ), which is then used in the value attribute of the inhomogeneousValue ( so for example condDensity = f( p ) )

VoltageClamp¶

class neuroml.nml.nml.VoltageClamp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, delay=None, duration=None, target_voltage=None, simple_series_resistance=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

VoltageClamp – Voltage clamp. Applies a variable current i to try to keep parent at targetVoltage. Not yet fully tested!!! Consider using voltageClampTriple!!

Parameters

delay (time) – Delay before change in current. Current is zero prior to this.
duration (time) – Duration for attempting to keep parent at targetVoltage. Current is zero after delay + duration.
targetVoltage (voltage) – Current will be applied to try to get parent to this target voltage
simpleSeriesResistance (resistance) – Current will be calculated by the difference in voltage between the target and parent, divided by this value

VoltageClampTriple¶

class neuroml.nml.nml.VoltageClampTriple(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, active=None, delay=None, duration=None, conditioning_voltage=None, testing_voltage=None, return_voltage=None, simple_series_resistance=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.Standalone

VoltageClampTriple – Voltage clamp with 3 clamp levels. Applies a variable current i ( through simpleSeriesResistance ) to try to keep parent cell at conditioningVoltage until time delay, testingVoltage until delay + duration, and returnVoltage afterwards. Only enabled if active = 1.

Parameters

active (none) – Whether the voltage clamp is active ( 1 ) or inactive ( 0 ).
delay (time) – Delay before switching from conditioningVoltage to testingVoltage.
duration (time) – Duration to hold at testingVoltage.
conditioningVoltage (voltage) – Target voltage before time delay
testingVoltage (voltage) – Target voltage between times delay and delay + duration
returnVoltage (voltage) – Target voltage after time duration
simpleSeriesResistance (resistance) – Current will be calculated by the difference in voltage between the target and parent, divided by this value

basePyNNCell¶

class neuroml.nml.nml.basePyNNCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.BaseCell

basePyNNCell – Base type of any PyNN standard cell model. Note: membrane potential v has dimensions voltage, but all other parameters are dimensionless. This is to facilitate translation to and from PyNN scripts in Python, where these parameters have implicit units, see http://neuralensemble.org/trac/PyNN/wiki/StandardModels

Parameters

cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

basePyNNIaFCell¶

class neuroml.nml.nml.basePyNNIaFCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNCell

basePyNNIaFCell – Base type of any PyNN standard integrate and fire model

Parameters

tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

basePyNNIaFCondCell¶

class neuroml.nml.nml.basePyNNIaFCondCell(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, extensiontype_=None, gds_collector_=None, **kwargs_)¶

Bases: neuroml.nml.nml.basePyNNIaFCell

basePyNNIaFCondCell – Base type of conductance based PyNN IaF cell models

Parameters

e_rev_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
e_rev_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_refrac (none) –
v_thresh (none) –
tau_m (none) –
v_rest (none) –
v_reset (none) –
cm (none) –
i_offset (none) –
tau_syn_E (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
tau_syn_I (none) – This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell
v_init (none) –

`loaders` Module¶

class neuroml.loaders.ArrayMorphLoader¶

Bases: object

classmethod load(filepath)¶: Right now this load method isn’t done in a very nice way. TODO: Complete refactoring.

class neuroml.loaders.NeuroMLHdf5Loader¶

Bases: object

classmethod load(src, optimized=False)¶

class neuroml.loaders.NeuroMLLoader¶

Bases: object

classmethod load(src)¶

class neuroml.loaders.SWCLoader¶

Bases: object

WARNING: Class defunct

classmethod load_swc_single(src, name=None)¶

neuroml.loaders.print_(text, verbose=True)¶

neuroml.loaders.read_neuroml2_file(nml2_file_name, include_includes=False, verbose=False, already_included=[], print_method=<function print_>, optimized=False)¶

neuroml.loaders.read_neuroml2_string(nml2_string, include_includes=False, verbose=False, already_included=[], print_method=<function print_>, optimized=False, base_path=None)¶

`writers` Module¶

class neuroml.writers.ArrayMorphWriter¶

Bases: object

For now just testing a simple method which can write a morphology, not a NeuroMLDocument.

classmethod write(data, filepath)¶

class neuroml.writers.NeuroMLHdf5Writer¶

Bases: object

classmethod write(nml_doc, h5_file_name, embed_xml=True, compress=True)¶

class neuroml.writers.NeuroMLWriter¶

Bases: object

classmethod write(nmldoc, file, close=True)¶: Writes from NeuroMLDocument to nml file in future can implement from other types via chain of responsibility pattern.

`utils` Module¶

Utilities for checking generated code

neuroml.utils.add_all_to_document(nml_doc_src, nml_doc_tgt, verbose=False)¶

Add all members of the source NeuroML document to the target NeuroML document.

Parameters

nml_doc_src (NeuroMLDocument) – source NeuroML document to copy from
nml_doc_tgt (NeuroMLDocument) – target NeuroML document to copy to
verbose (bool) – control verbosity of working

Raises

Exception – if a member could not be copied.

neuroml.utils.append_to_element(parent, child)¶

Append a child element to a parent Component

Parameters

parent (Object) – parent NeuroML component to add element to
child (Object) – child NeuroML component to be added to parent

Raises

Exception – when the child could not be added to the parent

neuroml.utils.get_summary(nml_file_name)¶

Get a summary of the given NeuroML file.

Parameters: nml_file_name (str) – name of NeuroML file to get summary of
Returns: summary of provided file
Return type: str

neuroml.utils.has_segment_fraction_info(connections)¶

Check if connections include fraction information

Parameters: connections (list) – list of connection objects
Returns: True if connections include fragment information, otherwise False
Return type: Boolean

neuroml.utils.is_valid_neuroml2(file_name)¶

Check if a file is valid NeuroML2.

Parameters: file_name (str) – name of NeuroML file to check
Returns: True if file is valid, False if not.
Return type: Boolean

neuroml.utils.main()¶

neuroml.utils.print_summary(nml_file_name)¶

Print a summary of the NeuroML model in the given file.

Parameters: nml_file_name (str) – name of NeuroML file to print summary of

neuroml.utils.validate_neuroml2(file_name)¶

Validate a NeuroML document against the NeuroML schema specification.

Parameters: file_name (str) – name of NeuroML file to validate.

`arraymorph` Module¶

Examples ¶

The examples in this section are intended to give in depth overviews of how to accomplish specific tasks with libNeuroML.

These examples are located in the neuroml/examples directory and can be tested to confirm they work by running the run_all.py script.

Examples

Examples

Creating a NeuroML morphology ¶

"""
Example of connecting segments together to create a 
multicompartmental model of a cell.
"""

import neuroml
import neuroml.writers as writers

p = neuroml.Point3DWithDiam(x=0, y=0, z=0, diameter=50)
d = neuroml.Point3DWithDiam(x=50, y=0, z=0, diameter=50)
soma = neuroml.Segment(proximal=p, distal=d)
soma.name = "Soma"
soma.id = 0

# Make an axon with 100 compartments:

parent = neuroml.SegmentParent(segments=soma.id)
parent_segment = soma
axon_segments = []
seg_id = 1

for i in range(100):
    p = neuroml.Point3DWithDiam(
        x=parent_segment.distal.x,
        y=parent_segment.distal.y,
        z=parent_segment.distal.z,
        diameter=0.1,
    )

    d = neuroml.Point3DWithDiam(
        x=parent_segment.distal.x + 10,
        y=parent_segment.distal.y,
        z=parent_segment.distal.z,
        diameter=0.1,
    )

    axon_segment = neuroml.Segment(proximal=p, distal=d, parent=parent)

    axon_segment.id = seg_id

    axon_segment.name = "axon_segment_" + str(axon_segment.id)

    # now reset everything:
    parent = neuroml.SegmentParent(segments=axon_segment.id)
    parent_segment = axon_segment
    seg_id += 1

    axon_segments.append(axon_segment)

test_morphology = neuroml.Morphology()
test_morphology.segments.append(soma)
test_morphology.segments += axon_segments
test_morphology.id = "TestMorphology"

cell = neuroml.Cell()
cell.name = "TestCell"
cell.id = "TestCell"
cell.morphology = test_morphology


doc = neuroml.NeuroMLDocument(id="TestNeuroMLDocument")

doc.cells.append(cell)

nml_file = "tmp/testmorphwrite.nml"

writers.NeuroMLWriter.write(doc, nml_file)

print("Written morphology file to: " + nml_file)

###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

Loading and modifying a file ¶

"""
In this example an axon is built, a morphology is loaded, the axon is
then connected to the loadeed morphology.
"""

import neuroml
import neuroml.loaders as loaders
import neuroml.writers as writers

fn = "./test_files/Purk2M9s.nml"
doc = loaders.NeuroMLLoader.load(fn)
print("Loaded morphology file from: " + fn)

# get the parent segment:
parent_segment = doc.cells[0].morphology.segments[0]

parent = neuroml.SegmentParent(segments=parent_segment.id)

# make an axon:
seg_id = 5000  # need a way to get a unique id from a morphology
axon_segments = []
for i in range(10):
    p = neuroml.Point3DWithDiam(
        x=parent_segment.distal.x,
        y=parent_segment.distal.y,
        z=parent_segment.distal.z,
        diameter=0.1,
    )

    d = neuroml.Point3DWithDiam(
        x=parent_segment.distal.x + 10,
        y=parent_segment.distal.y,
        z=parent_segment.distal.z,
        diameter=0.1,
    )

    axon_segment = neuroml.Segment(proximal=p, distal=d, parent=parent)

    axon_segment.id = seg_id

    axon_segment.name = "axon_segment_" + str(axon_segment.id)

    # now reset everything:
    parent = neuroml.SegmentParent(segments=axon_segment.id)
    parent_segment = axon_segment
    seg_id += 1

    axon_segments.append(axon_segment)

doc.cells[0].morphology.segments += axon_segments

nml_file = "./tmp/modified_morphology.nml"

writers.NeuroMLWriter.write(doc, nml_file)

print("Saved modified morphology file to: " + nml_file)


###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

Building a network ¶

"""

Example to build a full spiking IaF network
through libNeuroML, save it as XML and validate it

"""

from neuroml import NeuroMLDocument
from neuroml import IafCell
from neuroml import Network
from neuroml import ExpOneSynapse
from neuroml import Population
from neuroml import PulseGenerator
from neuroml import ExplicitInput
from neuroml import SynapticConnection
import neuroml.writers as writers
from random import random


nml_doc = NeuroMLDocument(id="IafNet")

IafCell0 = IafCell(
    id="iaf0",
    C="1.0 nF",
    thresh="-50mV",
    reset="-65mV",
    leak_conductance="10 nS",
    leak_reversal="-65mV",
)

nml_doc.iaf_cells.append(IafCell0)

IafCell1 = IafCell(
    id="iaf1",
    C="1.0 nF",
    thresh="-50mV",
    reset="-65mV",
    leak_conductance="20 nS",
    leak_reversal="-65mV",
)

nml_doc.iaf_cells.append(IafCell1)

syn0 = ExpOneSynapse(id="syn0", gbase="65nS", erev="0mV", tau_decay="3ms")

nml_doc.exp_one_synapses.append(syn0)

net = Network(id="IafNet")

nml_doc.networks.append(net)

size0 = 5
pop0 = Population(id="IafPop0", component=IafCell0.id, size=size0)

net.populations.append(pop0)

size1 = 5
pop1 = Population(id="IafPop1", component=IafCell0.id, size=size1)

net.populations.append(pop1)

prob_connection = 0.5

for pre in range(0, size0):

    pg = PulseGenerator(
        id="pulseGen_%i" % pre,
        delay="0ms",
        duration="100ms",
        amplitude="%f nA" % (0.1 * random()),
    )

    nml_doc.pulse_generators.append(pg)

    exp_input = ExplicitInput(target="%s[%i]" % (pop0.id, pre), input=pg.id)

    net.explicit_inputs.append(exp_input)

    for post in range(0, size1):
        # fromxx is used since from is Python keyword
        if random() <= prob_connection:
            syn = SynapticConnection(
                from_="%s[%i]" % (pop0.id, pre),
                synapse=syn0.id,
                to="%s[%i]" % (pop1.id, post),
            )
            net.synaptic_connections.append(syn)

nml_file = "tmp/testnet.nml"
writers.NeuroMLWriter.write(nml_doc, nml_file)


print("Written network file to: " + nml_file)


###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

Building a 3D network ¶

"""

Example to build a full spiking IaF network throught libNeuroML & save it as XML & validate it

"""

from neuroml import NeuroMLDocument
from neuroml import Network
from neuroml import ExpOneSynapse
from neuroml import Population
from neuroml import Property
from neuroml import Cell
from neuroml import Location
from neuroml import Instance
from neuroml import Morphology
from neuroml import Point3DWithDiam
from neuroml import Segment
from neuroml import SegmentParent
from neuroml import Projection
from neuroml import Connection

import neuroml.writers as writers
from random import random

soma_diam = 10
soma_len = 10
dend_diam = 2
dend_len = 10
dend_num = 10


def generateRandomMorphology():

    morphology = Morphology()

    p = Point3DWithDiam(x=0, y=0, z=0, diameter=soma_diam)
    d = Point3DWithDiam(x=soma_len, y=0, z=0, diameter=soma_diam)
    soma = Segment(proximal=p, distal=d, name="Soma", id=0)

    morphology.segments.append(soma)
    parent_seg = soma

    for dend_id in range(0, dend_num):

        p = Point3DWithDiam(x=d.x, y=d.y, z=d.z, diameter=dend_diam)
        d = Point3DWithDiam(x=p.x, y=p.y + dend_len, z=p.z, diameter=dend_diam)
        dend = Segment(proximal=p, distal=d, name="Dend_%i" % dend_id, id=1 + dend_id)
        dend.parent = SegmentParent(segments=parent_seg.id)
        parent_seg = dend

        morphology.segments.append(dend)

    morphology.id = "TestMorphology"

    return morphology


def run():

    cell_num = 10
    x_size = 500
    y_size = 500
    z_size = 500

    nml_doc = NeuroMLDocument(id="Net3DExample")

    syn0 = ExpOneSynapse(id="syn0", gbase="65nS", erev="0mV", tau_decay="3ms")
    nml_doc.exp_one_synapses.append(syn0)

    net = Network(id="Net3D")
    nml_doc.networks.append(net)

    proj_count = 0
    # conn_count = 0

    for cell_id in range(0, cell_num):

        cell = Cell(id="Cell_%i" % cell_id)

        cell.morphology = generateRandomMorphology()

        nml_doc.cells.append(cell)

        pop = Population(
            id="Pop_%i" % cell_id, component=cell.id, type="populationList"
        )
        net.populations.append(pop)
        pop.properties.append(Property(tag="color", value="1 0 0"))

        inst = Instance(id="0")
        pop.instances.append(inst)

        inst.location = Location(
            x=str(x_size * random()), y=str(y_size * random()), z=str(z_size * random())
        )

        prob_connection = 0.5
        for post in range(0, cell_num):
            if post is not cell_id and random() <= prob_connection:

                from_pop = "Pop_%i" % cell_id
                to_pop = "Pop_%i" % post

                pre_seg_id = 0
                post_seg_id = 1

                projection = Projection(
                    id="Proj_%i" % proj_count,
                    presynaptic_population=from_pop,
                    postsynaptic_population=to_pop,
                    synapse=syn0.id,
                )
                net.projections.append(projection)
                connection = Connection(
                    id=proj_count,
                    pre_cell_id="%s[%i]" % (from_pop, 0),
                    pre_segment_id=pre_seg_id,
                    pre_fraction_along=random(),
                    post_cell_id="%s[%i]" % (to_pop, 0),
                    post_segment_id=post_seg_id,
                    post_fraction_along=random(),
                )

                projection.connections.append(connection)
                proj_count += 1
                # net.synaptic_connections.append(SynapticConnection(from_="%s[%i]"%(from_pop,0),  to="%s[%i]"%(to_pop,0)))

    #######   Write to file  ######

    nml_file = "tmp/net3d.nml"
    writers.NeuroMLWriter.write(nml_doc, nml_file)

    print("Written network file to: " + nml_file)

    ###### Validate the NeuroML ######

    from neuroml.utils import validate_neuroml2

    validate_neuroml2(nml_file)


run()

Ion channels ¶

"""
Generating a Hodgkin-Huxley Ion Channel and writing it to NeuroML
"""

import neuroml
import neuroml.writers as writers

chan = neuroml.IonChannelHH(
    id="na",
    conductance="10pS",
    species="na",
    notes="This is an example voltage-gated Na channel",
)

m_gate = neuroml.GateHHRates(id="m", instances="3")
h_gate = neuroml.GateHHRates(id="h", instances="1")

m_gate.forward_rate = neuroml.HHRate(
    type="HHExpRate", rate="0.07per_ms", midpoint="-65mV", scale="-20mV"
)

m_gate.reverse_rate = neuroml.HHRate(
    type="HHSigmoidRate", rate="1per_ms", midpoint="-35mV", scale="10mV"
)

h_gate.forward_rate = neuroml.HHRate(
    type="HHExpLinearRate", rate="0.1per_ms", midpoint="-55mV", scale="10mV"
)

h_gate.reverse_rate = neuroml.HHRate(
    type="HHExpRate", rate="0.125per_ms", midpoint="-65mV", scale="-80mV"
)

chan.gate_hh_rates.append(m_gate)
chan.gate_hh_rates.append(h_gate)

doc = neuroml.NeuroMLDocument()
doc.ion_channel_hhs.append(chan)

doc.id = "ChannelMLDemo"

nml_file = "./tmp/ionChannelTest.xml"
writers.NeuroMLWriter.write(doc, nml_file)

print("Written channel file to: " + nml_file)


###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

PyNN models ¶

"""

Example to build a PyNN based network

"""

from neuroml import NeuroMLDocument
from neuroml import *
import neuroml.writers as writers
from random import random


########################   Build the network   ####################################

nml_doc = NeuroMLDocument(id="IafNet")


pynn0 = IF_curr_alpha(
    id="IF_curr_alpha_pop_IF_curr_alpha",
    cm="1.0",
    i_offset="0.9",
    tau_m="20.0",
    tau_refrac="10.0",
    tau_syn_E="0.5",
    tau_syn_I="0.5",
    v_init="-65",
    v_reset="-62.0",
    v_rest="-65.0",
    v_thresh="-52.0",
)
nml_doc.IF_curr_alpha.append(pynn0)

pynn1 = HH_cond_exp(
    id="HH_cond_exp_pop_HH_cond_exp",
    cm="0.2",
    e_rev_E="0.0",
    e_rev_I="-80.0",
    e_rev_K="-90.0",
    e_rev_Na="50.0",
    e_rev_leak="-65.0",
    g_leak="0.01",
    gbar_K="6.0",
    gbar_Na="20.0",
    i_offset="0.2",
    tau_syn_E="0.2",
    tau_syn_I="2.0",
    v_init="-65",
    v_offset="-63.0",
)
nml_doc.HH_cond_exp.append(pynn1)

pynnSynn0 = ExpCondSynapse(id="ps1", tau_syn="5", e_rev="0")
nml_doc.exp_cond_synapses.append(pynnSynn0)

nml_file = "tmp/pynn_network.xml"
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Saved to: " + nml_file)


###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

Synapses ¶

"""

Example to create a file with multiple synapse types

"""

from neuroml import NeuroMLDocument
from neuroml import *
import neuroml.writers as writers
from random import random


nml_doc = NeuroMLDocument(id="SomeSynapses")

expOneSyn0 = ExpOneSynapse(id="ampa", tau_decay="5ms", gbase="1nS", erev="0mV")
nml_doc.exp_one_synapses.append(expOneSyn0)

expTwoSyn0 = ExpTwoSynapse(
    id="gaba", tau_decay="12ms", tau_rise="3ms", gbase="1nS", erev="-70mV"
)
nml_doc.exp_two_synapses.append(expTwoSyn0)

bpSyn = BlockingPlasticSynapse(
    id="blockStpSynDep", gbase="1nS", erev="0mV", tau_rise="0.1ms", tau_decay="2ms"
)
bpSyn.notes = "This is a note"
bpSyn.plasticity_mechanism = PlasticityMechanism(
    type="tsodyksMarkramDepMechanism", init_release_prob="0.5", tau_rec="120 ms"
)
bpSyn.block_mechanism = BlockMechanism(
    type="voltageConcDepBlockMechanism",
    species="mg",
    block_concentration="1.2 mM",
    scaling_conc="1.920544 mM",
    scaling_volt="16.129 mV",
)

nml_doc.blocking_plastic_synapses.append(bpSyn)


nml_file = "tmp/synapses.xml"
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Saved to: " + nml_file)


###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

Working with JSON serialization ¶

One thing to note is that the JSONWriter, unlike NeuroMLWriter, will serializing using array-based (Arraymorph) representation if this has been used.

Working with arraymorphs ¶

"""
Example of connecting segments together to create a 
multicompartmental model of a cell.

In this case ArrayMorphology will be used rather than
Morphology - demonstrating its similarity and
ability to save in HDF5 format
"""

import neuroml
import neuroml.writers as writers
import neuroml.arraymorph as am

p = neuroml.Point3DWithDiam(x=0, y=0, z=0, diameter=50)
d = neuroml.Point3DWithDiam(x=50, y=0, z=0, diameter=50)
soma = neuroml.Segment(proximal=p, distal=d)
soma.name = "Soma"
soma.id = 0

# now make an axon with 100 compartments:

parent = neuroml.SegmentParent(segments=soma.id)
parent_segment = soma
axon_segments = []
seg_id = 1
for i in range(100):
    p = neuroml.Point3DWithDiam(
        x=parent_segment.distal.x,
        y=parent_segment.distal.y,
        z=parent_segment.distal.z,
        diameter=0.1,
    )

    d = neuroml.Point3DWithDiam(
        x=parent_segment.distal.x + 10,
        y=parent_segment.distal.y,
        z=parent_segment.distal.z,
        diameter=0.1,
    )

    axon_segment = neuroml.Segment(proximal=p, distal=d, parent=parent)

    axon_segment.id = seg_id

    axon_segment.name = "axon_segment_" + str(axon_segment.id)

    # now reset everything:
    parent = neuroml.SegmentParent(segments=axon_segment.id)
    parent_segment = axon_segment
    seg_id += 1

    axon_segments.append(axon_segment)

test_morphology = am.ArrayMorphology()
test_morphology.segments.append(soma)
test_morphology.segments += axon_segments
test_morphology.id = "TestMorphology"

cell = neuroml.Cell()
cell.name = "TestCell"
cell.id = "TestCell"
cell.morphology = test_morphology


doc = neuroml.NeuroMLDocument()
# doc.name = "Test neuroML document"

doc.cells.append(cell)
doc.id = "TestNeuroMLDocument"

nml_file = "tmp/arraymorph.nml"

writers.NeuroMLWriter.write(doc, nml_file)

print("Written morphology file to: " + nml_file)

###### Validate the NeuroML ######

from neuroml.utils import validate_neuroml2

validate_neuroml2(nml_file)

Working with Izhikevich Cells ¶

These examples were kindly contributed by Steve Marsh

# from neuroml import NeuroMLDocument
from neuroml import IzhikevichCell
from neuroml.loaders import NeuroMLLoader
from neuroml.utils import validate_neuroml2


def load_izhikevich(filename="./test_files/SingleIzhikevich.nml"):
    nml_filename = filename
    validate_neuroml2(nml_filename)
    nml_doc = NeuroMLLoader.load(nml_filename)

    iz_cells = nml_doc.izhikevich_cells
    for i, iz in enumerate(iz_cells):
        if isinstance(iz, IzhikevichCell):
            neuron_string = "%d %s %s %s %s %s (%s)" % (
                i,
                iz.v0,
                iz.a,
                iz.b,
                iz.c,
                iz.d,
                iz.id,
            )
            print(neuron_string)
        else:
            print("Error: Cell %d is not an IzhikevichCell" % i)


load_izhikevich()

from neuroml import NeuroMLDocument
from neuroml import IzhikevichCell
from neuroml.writers import NeuroMLWriter
from neuroml.utils import validate_neuroml2


def write_izhikevich(filename="./tmp/SingleIzhikevich_test.nml"):
    nml_doc = NeuroMLDocument(id="SingleIzhikevich")
    nml_filename = filename

    iz0 = IzhikevichCell(
        id="iz0", v0="-70mV", thresh="30mV", a="0.02", b="0.2", c="-65.0", d="6"
    )

    nml_doc.izhikevich_cells.append(iz0)

    NeuroMLWriter.write(nml_doc, nml_filename)
    validate_neuroml2(nml_filename)


write_izhikevich()

References¶

VCC+14: Michael Vella, Robert C. Cannon, Sharon Crook, Andrew P. Davison, Gautham Ganapathy, Hugh P. C. Robinson, R. Angus Silver, and Padraig Gleeson. Libneuroml and pylems: using python to combine procedural and declarative modeling approaches in computational neuroscience. Frontiers in neuroinformatics, 8:38, 2014. doi:10.3389/fninf.2014.00038.

Contributing¶

How to contribute¶

libNeuroML development happens on GitHub, so you will need a GitHub account to contribute to the repository. Contributions are made using the standard Pull Request workflow.

Setting up¶

Please take a look at the GitHub documentation here: http://help.github.com/fork-a-repo/

To begin, please fork the repo on the GitHub website. You should now have a libNeuroML under you username. Next, we clone our fork to get a local copy on our computer:

git clone git@github.com:_username_/libNeuroML.git

While not necessary, it is good practice to add the upstream repository as a remote that you will follow:

cd libNeuroML
git remote add upstream https://github.com/NeuralEnsemble/libNeuroML.git
git fetch upstream

You can check which branch are you following doing:

git branch -a

You should have something like:

git branch -a
* master
  remotes/origin/HEAD -> origin/master
  remotes/origin/master
  remotes/upstream/master

Sync with upstream¶

Before starting to do some work, please check to see that you have the latest copy of the sources in your local repository:

git fetch upstream
git checkout development
git merge upstream/development

Working locally on a dedicated branch¶

Now that we have a fork, we can start making our changes to the source code. The best way to do it is to create a branch with a descriptive name to indicate what are you working on. Generally, your will branch off from the upstream development branch, which will contain the latest code.

For example, just for the sake of this guide, I’m going to work on issue #2.

git checkout development
git checkout -b fix-2

We can work in this branch, and make as many commits as we need to:

# hack hack hack
git commit -am "some decent commit message here"

Once we have finished working, we can push the branch online to our fork:

git push origin fix-2

We can then open a pull-request to merge our fix-2 branch into upstream/development. If your code is not ready to be included, you can update the code on your branch and any more commits you add there will be added to the Pull Request. Members of the libNeuroML development team will then discuss your changes with you, perhaps suggest tweaks, and then merge it when ready.

Continuous integration¶

libNeuroML uses continuous integration (Wikipedia). Each commit to the master or development branches is tested, along with all commits to pull requests. The latest status of the continuous integration tests can be seen here on GitHub Actions.

Release process¶

libNeuroML is part of the official NeuroML release cycle. When a new libNeuroML release is ready the following needs to happen:

Update version number in setup.py
update version number in doc/conf.py
update release number in doc/conf.py (same as version number)
update changelog in README.md
merge development branch with master (This should happen via pull request - do not do the merge yourself even if you are an owner of the repository.
push latest release to PyPi

More information on the NeuroML release process can be found on the NeuroML documentation page.

Regenerating documentation¶

Please create a virtual environment and use the requirements.txt file to install the necessary bits.

In most cases, running make html should be sufficient to regenerate the documentation. However, if any changes to nml.py have been made, the nml-core-docs.py file in the helpers directory will also need to be run. This script manually adds each class from nml.py to the documentation as a sub-section using the autoclass sphinx directive instead of the automodule directive which does not allow us to do this.

Implementation of XML bindings for libNeuroML¶

The GenerateDS Python package is used to automatically generate the NeuroML XML-bindings in libNeuroML from the NeuroML Schema. This technique can be utilized for any XML Schema and is outlined in this section. The addition of helper methods and enforcement of correct naming conventions is also described. For more detail on how Python bindings for XML are generated, the reader is directed to the GenerateDS and libNeuroML documentation. In the following subsections it is assumed that all commands are executed in a top level directory nml and that GenerateDS is installed. It should be noted that enforcement of naming conventions and addition of helper methods are not required by GenerateDS and default values may be used.

Correct naming conventions¶

A module named generateds_config.py is placed in the nml directory. This module contains a Python dictionary called NameTable which maps the original names specified in the XML Schema to user-specified ones. The NameTable dictionary can be defined explicitly or generated programmatically, for example using regular expressions.

Addition of helper methods¶

Helper methods associated with a class can be added to a Python module as string objects. In the case of libNeuroML the module is called helper_methods.py. The precise implementation details are esoteric and the user is referred to the GenerateDS documentation for details of how this functionality is implemented.

Generation of bindings¶

Once generateds_config.py and a helper methods module are present in the nml directory a valid XML Schema is required by GenerateDS. The following command generates the nml.py module which contains the XML-bindings:

$ generateDS.py -o nml.py --use-getter-setter=none --user-methods=helper_methods NeuroML_v2beta1.xsd

The -o flag sets the file which the module containing the bindings is to be written to. The –use-getter-setter=none option disables getters and setters for class attributes. The –user-methods flag indicates the name of the helper methods module (See section “Addition of helper methods”). The final parameter (NeuroML_v2beta1.xsd) is the name of the XML Schema used for generating the bindings.

Multicompartmental Python API Meeting¶

Organisation¶

Dates: 25 & 26 June 2012

Location: Room 336, Rockefeller building, UCL, London

Attendees: Sandra Berger, Andrew Davison, Padraig Gleeson, Mike Hull, Steve Marsh, Michele Mattioni, Eugenio Piasini, Mike Vella

Sponsors: This meeting was generously supported by the INCF Multi Scale Modelling Program.

Minutes¶

Agreeing on terminology (segments, etc.) & scope¶

A discussion on the definitions of the key terms Node, Segment and Section is here, and was the basis for discussions on these definitions at the meeting:

Nodes, Segments and Sections

Agreements

The Python libNeuroML API will use Node as a key building block for morphologies.

Segment is agreed on as the basis for defining morphologies in NeuroML and will be a top level object in libNeuroML, where it will be the part of a neurite between two Nodes (proximal & distal).

Segment Group will be the basis for the grouping of these, and will be used to define dendrites, axons, etc.

Section is a term for the cable-like building block in NEURON, and will not be formally used in NeuroML or libNeuroML.

There was a discussion on whether it would be useful to be able to include this concept “by the back door” to enable lossless import & export of morphologies from NEURON. Padraig’s proposal was to add an attribute (e.g. primary) to the segmentGroup element to flag a core set of non overlapping segmentGroups, which are continuous (all children are connected to distal point of parent) which would correspond to the old “cable” concept in NeuroML v1.x.

There was much discussion on the usefulness of this concept and whether it should be a different element/object in the API from segmentGroup. The outcome was not fully resolved, but as a first test of this concept, Padraig will add the new attribute to NeuroML, Mike V will add a flag (boolean?) to the API, and at a later point, when the API begins to interact with native simulators, we can reevaluate the usefulness of the term.

Mike Vella’s current implementation¶

This is under development at: https://github.com/NeuralEnsemble/libNeuroML/tree/master/neuroml

Mike will continue on this (almost) full time for the next 2 months.

Following the meeting, he will perform a refactoring operation on the code base to better reflect the names used in NeuroML, e.g.

neuroml_doc

cells

morphology # not entirely sure how this works- contains segment groups and is itself a segment group?

segments

segment_groups

segment_groups

biophysical_properties

notes

morphologies

networks

point currents

ion channels

synapses

extracellular properties

It was also decided that certain SegmentGroup names should have reserved names in libNeuroML, the exact implementation of this is undecided:

Segment groups with reserved names:: soma_group

axon_group

apical_dendrite_group

basal_dendrite_group

It was also decided that a segment should only be able to connect to the root of a morphology, the syntax should be something along the lines of:

segment can only connect to root of a morphology

connect syntax examples:

morph2.attach(2,cell2,0.5) (default frac along = None)

and:

morph[2].attach(cell2,0.5)

Mike V was asked to add a clone method to a morphology.

It was decided that fraction_along should be a property of segment.

The syntax for segment groups should be as follows: group=morph.segment_groups[‘axon_group’] (in connect merge groups should be false by default - throw an exception, tell the user setting merge_groups = True or rename group will fix this)

This was a subject of great debate and has not been completely settled.

Morphforge latest developments¶

Mike Hull gave a brief overview of the latest developments with Morphforge:

https://github.com/mikehulluk/morphforge

He pointed out that it’s still undergoing refactoring, but it can be used by other interested parties, and there is detailed documentation online regarding installation, examples, etc.

Neuronvisio latest developments¶

Michele Mattioni gave a status update on Neuronvisio:

http://neuronvisio.org

The application has been closely linked to the NEURON simulator but hopefully use of libNeuroML will allow it to be used independently of NEURON.

Michele showed Neuronvisio’s native HDF5 format as just one possible way to encode model structure + simulation results: https://github.com/NeuralEnsemble/libNeuroML/blob/master/hdf5Examples/Neuronvisio_medium_cell_example_10ms.h5

Current Python & NeuroML support in MOOSE¶

A Skype call/Google Hangout was held on Tues at 9:30 to get an update from Bangalore.

The slides from this discussion are here:

https://github.com/NeuralEnsemble/libNeuroML/blob/master/doc/2012_06_26_neuroml_with_pymoose.pdf

As outlined there there are a number of areas in which MOOSE and Moogli import/export NeuroML version 1.x. A number of issues and desired features missing in v1.x were highlighted, most of which are implemented or planned for NeuroML v2.0.

There was general enthusiasm about the libNeuroML project, and it was felt that MOOSE should eventually transition to using libNeuroML to import NeuroML models. This will happen in parallel with updating of the MOOSE PyNN implementation.

The MOOSE developers were also keen to see how the new ComponentTypes in NeuroML 2 will map to inbuilt objects in MOOSE (e.g. Integrate-and-Fire neurons, Markov channel, Izhikevich). They will add simple examples to the latest MOOSE code to demonstrate their current implementation and discussion can continue on the mailing lists.

Saving to & loading from XML¶

There was not any detailed discussion on the various strategies for reading/saving XML in Python.

Padraig’s suggestion based on generateDS.py: https://github.com/NeuralEnsemble/libNeuroML/tree/master/ideas/padraig/generatedFromV2Schema produces a very big file, which while usable as an API, e.g. see:

https://github.com/NeuralEnsemble/libNeuroML/blob/master/hhExample/hh_NEUROML2.py

could do with a lot of refactoring. It was felt that a version of this with a very efficient description of morphologies (and network instances) based on the current work of Mike V is the way forward.

Storing simulation data as HDF5¶

The examples at: https://github.com/NeuralEnsemble/libNeuroML/tree/master/hdf5Examples have been updated.

The long term aim would be to arrive at a common format here that can be saved by simulators and that visualisation packages like Moogli and Neuronvisio can read and display. This may be based on Neo: http://packages.python.org/neo/, but that package’s current lack of ability to deal with data with nonuniform time points (e.g. produced by variable time step simulations) may be a limiting factor.

General PyNN & NeuroML v2.0 interoperability¶

There was agreement that libNeuroML will form the basis of the multicompartmental neuron support in PyNN. The extra functionality needed to interact with simulators is currently termed “Pyramidal”, but this will eventually be fully merged into PyNN.

http://neuralensemble.org/trac/PyNN http://www.neuroml.org/NeuroML2CoreTypes/PyNN.html http://www.neuroml.org/pynn.php

Nodes, Segments and Sections¶

An attempt to clarify these interrelated terms used in describing morphologies. Names in bold type are used for elements of the NeuroML object model.

Nodes¶

A node is a 3D point with diameter information which forms the basis for 3D morphological reconstructions.

These nodes (or points) are the fundamental building blocks in the SWC and Neurolucida formats. This method of description is based on the assumption that each node is physically connected to another node.

Segments¶

A segment (according to NeuroML v1&2) is a part of a neuronal tree between two 3D points with diameters (proximal & distal). The term node isn’t used in NeuroML but the above description describes perfectly well the proximal & distal points. Cell morphology elements consist of lists of segments (each with unique integer id, and optional name).

All segments, apart from the root segment, have a parent segment. If the proximal point of the segment is not specified, the distal point of the parent segment is used for the proximal point of the child.

A special case is defined where proximal == distal, and the segment is assumed to be a sphere at that location with the specified diameter.

Segments can be grouped into segmentGroups in NeuroML v2.0. These can be used to specify “apical_dendrites”, “axon_group”, etc., which in turn can be used for placing channels on the cell.

An example of a NeuroML v2.0 cell is here.

libNeuroML will allow low level access to create and modify morphologies by handling nodes. Segments will also be top level objects in the API. The XML serialisation will only specify segments with proximal & distal points, but the HDF5 version may have an efficient serialisation of nodes & segments.

Sections¶

The concept of section is fundamentally important in NEURON. A section in this simulator is an unbranched cable which can have multiple 3D points outlining the structure of a neurite in 3D. These points are used to determine the surface area along the section. NEURON can vary the spatial discretisation of the neurite by varying the “nseg” value of the section, e.g. a section with 20 3D points and nseg =4 will be split into 4 parts of equal length for simulating (as isopotential compartments), with the surface area (and so total channel conductance) of each determined by the set of 3D points in that part.

There was a similar concept to this in NeuroML v1.x, the cable. Each segment had an attribute for the cable id, and these were used for mapping to and from NEURON. Cables were unbranched, and so all segments after the first in the cable only had distal points, see this example.

The cable concept was removed in NeuroML v2.0, as this is was seen as imposing concepts from compartmental modelling on the basic morphological descriptions of cells. There is only a segmentGroup element for grouping segments, though a segment can belong to multiple segmentGroups, which don’t need to be unbranched (unlike cables). There may need to be a new attribute in segmentGroup (e.g. primary or unbranched or cable=”true”) which defines a nonoverlapping set of unbranched segmentGroups, which can be used as the basis for sections in any parsing application which is interested in them, or be ignored by any other application.

In libNeuroML, a section-like concept can be added at API level, to facilitate building cells, to facilitate import/export to/from simulators supporting this concept, and to serve as a basis for recompartmentalisation of cells.

Issues¶

Dendrites in space¶

One major issue to address is that in many neuronal reconstructions, the soma is not included (or perhaps just an outline of the soma is given), only the dendrites are. These dendrites’ 3D start points are on the edge of the soma membrane “floating in space”. Normal procedure for a modeller in this case is to create a spherical soma at this central point and electrically attach the dendrites to the centre of this.

In this case (and many others) the physical location of the start of the child segments do not correspond to the electrical (or logical) connection point on the parent. This has advantages and disadvantages:

(+) It allows the real 3D points of the neuronal reconstruction to be retained (useful for visualisation)

(-) This is not unambiguously captured in the simplest morphological formats like SWC, which assume physical connectivity between nodes/points

This scenario is supported in NeuroML v1&2, where a child segment has the option to redefine its start point (by adding a proximal) with the child <-> parent relationship defining the electrical connection. This allows lossless import & export from NEURON and removes the ambiguity of more compact formats like SWC and Neurolucida.

Connections mid segment¶

Another option for electrical connections (also influences by NEURON sections) is the ability for segments to (electrically/logically) connect to a point inside a segment. This is specified by adding a fractionAlong attribute to the parent element, i.e.

<parent segment="2" fractionAlong="0.5"/>

This is not possible in a node based format, but represents a logically consistent description of what the modeller wants.

What to do?¶

Two options are available then for a serialisation format or API: should it try to support all of these scenarios, or try to enforce “best practice”?

PG: I’d argue for the first approach, as it retains as much as possible of what the original reconstructor/simulator specified. An API which enforces a policy when it encounters a non optimal morphology (e.g. moving all dendrites to connection points, inserting new nodes) will alter the original data in perhaps unintended ways, and that information will be lost by subsequent readers. It should be up to each parsing application to decide what to do with the extra information when it reads in a file.

libNeuroML: documentation¶

User guide¶

Introduction¶

NeuroML¶

Serialisations¶

Installation¶

Using Pip¶

On Fedora based systems¶

Install from source¶

Run an example¶

Unit tests¶

API documentation¶

nml Module (NeuroML Core classes)¶

List of Component classes¶

AdExIaFCell¶

AlphaCondSynapse¶

AlphaCurrSynapse¶

AlphaCurrentSynapse¶

AlphaSynapse¶

Annotation¶

Base¶

BaseCell¶

BaseCellMembPotCap¶

BaseConductanceBasedSynapse¶

BaseConductanceBasedSynapseTwo¶

BaseConnection¶

BaseConnectionNewFormat¶

BaseConnectionOldFormat¶

BaseCurrentBasedSynapse¶

BaseNonNegativeIntegerId¶

BaseProjection¶

BasePynnSynapse¶

BaseSynapse¶

BaseVoltageDepSynapse¶

BaseWithoutId¶

BiophysicalProperties¶

BiophysicalProperties2CaPools¶

BlockMechanism¶

BlockingPlasticSynapse¶

Case¶

Cell¶

Cell2CaPools¶

CellSet¶

ChannelDensity¶

ChannelDensityGHK¶

ChannelDensityGHK2¶

ChannelDensityNernst¶

ChannelDensityNernstCa2¶

ChannelDensityNonUniform¶

ChannelDensityNonUniformGHK¶

ChannelDensityNonUniformNernst¶

ChannelDensityVShift¶

ChannelPopulation¶

ClosedState¶

ComponentType¶

CompoundInput¶

CompoundInputDL¶

ConcentrationModel_D¶

ConditionalDerivedVariable¶

Connection¶

ConnectionWD¶

Constant¶

ContinuousConnection¶

ContinuousConnectionInstance¶

ContinuousConnectionInstanceW¶

ContinuousProjection¶

DecayingPoolConcentrationModel¶

DerivedVariable¶

DistalDetails¶

DoubleSynapse¶

Dynamics¶

EIF_cond_alpha_isfa_ista¶

EIF_cond_exp_isfa_ista¶

ElectricalConnection¶

ElectricalConnectionInstance¶

ElectricalConnectionInstanceW¶

ElectricalProjection¶

ExpCondSynapse¶

ExpCurrSynapse¶

ExpOneSynapse¶

`nml` Module (NeuroML Core classes)¶