`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) –

API documentation

loaders Module

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¶

ExpThreeSynapse¶

ExpTwoSynapse¶

ExplicitInput¶

Exposure¶

ExtracellularProperties¶

ExtracellularPropertiesLocal¶

FitzHughNagumo1969Cell¶

FitzHughNagumoCell¶

FixedFactorConcentrationModel¶

ForwardTransition¶

GapJunction¶

GateFractional¶

`nml` Module (NeuroML Core classes)¶