Inputs

Inputs#

A number of ComponentTypes for providing spiking ( e.g. spikeGeneratorPoisson, spikeArray ) and current inputs ( e.g. pulseGenerator, voltageClamp, timedSynapticInput, poissonFiringSynapse ) to other ComponentTypes

Original ComponentType definitions: Inputs.xml. Schema against which NeuroML based on these should be valid: NeuroML_v2.3.xsd. Generated on 14/08/24 from this commit. Please file any issues or questions at the issue tracker here.

basePointCurrent#

extends baseStandalone

Base type for all ComponentTypes which produce a current i ( with dimension current ).

Exposures

i

The total (usually time varying) current produced by this ComponentType

current

baseVoltageDepPointCurrent#

extends basePointCurrent

Base type for all ComponentTypes which produce a current i ( with dimension current ) and require a voltage v exposed on the parent Component, which would often be the membrane potential of a Component extending baseCellMembPot.

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed

voltage

baseVoltageDepPointCurrentSpiking#

extends baseVoltageDepPointCurrent

Base type for all ComponentTypes which produce a current i, require a membrane potential v exposed on the parent and emit spikes ( on a port spike ). The exposed variable tsince can be used for plotting the time since the Component has spiked last.

Exposures

i	The total (usually time varying) current produced by this ComponentType (from basePointCurrent)	current
tsince	Time since the last spike was emitted	time

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepPointCurrent)

voltage

Event Ports

spike

Port on which spikes are emitted

Direction: out

basePointCurrentDL#

Base type for all ComponentTypes which produce a dimensionless current I. There are many dimensionless equivalents of all the core current producing ComponentTypes such as pulseGenerator / pulseGeneratorDL, sineGenerator / sineGeneratorDL and rampGenerator / rampGeneratorDL.

Exposures

I

The total (time varying) current produced by this ComponentType

Dimensionless

baseVoltageDepPointCurrentDL#

extends basePointCurrentDL

Base type for all ComponentTypes which produce a dimensionless current I and require a dimensionless membrane potential V exposed on the parent Component.

Exposures

I

The total (time varying) current produced by this ComponentType (from basePointCurrentDL)

Dimensionless

Requirements

V

The current may vary with the dimensionless voltage exposed by the ComponentType on which this is placed

Dimensionless

baseSpikeSource#

Base for any ComponentType whose main purpose is to emit spikes ( on a port spike ). The exposed variable tsince can be used for plotting the time since the Component has spiked last.

Exposures

tsince

Time since the last spike was emitted

time

Event Ports

spike

Port on which spikes are emitted

Direction: out

spikeGenerator#

extends baseSpikeSource

Simple generator of spikes at a regular interval set by period.

Parameters

period

Time between spikes. The first spike will be emitted after this time.

time

Constants

SMALL_TIME = 1e-9ms

A useful constant for use as a non zero time increment

time

Exposures

tnext	When the next spike should ideally be emitted (dt permitting)	time
tsince	Time since the last spike was emitted (from baseSpikeSource)	time

Event Ports

spike

Port on which spikes are emitted (from baseSpikeSource)

Direction: out

Dynamics

State Variables: tsince: time (exposed as tsince); tnext: time (exposed as tnext)
On Start: tsince = 0; tnext = period
On Conditions: IF tnext - t < SMALL_TIME THEN; tsince = 0; tnext = tnext+period; EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1; d tnext /dt = 0

Schema

<xs:complexType name="SpikeGenerator">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="period" type="Nml2Quantity_time" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SpikeGenerator
from neuroml.utils import component_factory

variable = component_factory(
    SpikeGenerator,
    id: 'a NonNegativeInteger (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    period: 'a Nml2Quantity_time (required)' = None,
)

Usage: XML

<spikeGenerator id="spikeGenRegular" period="20 ms"/>

spikeGeneratorRandom#

extends baseSpikeSource

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

Parameters

maxISI	Maximum interspike interval	time
minISI	Minimum interspike interval	time

Constants

MSEC = 1ms

Required for converting time values to/from dimensionless quantities

time

Exposures

isi	The interval until the next spike	time
tnext	When the next spike should ideally be emitted (dt permitting)	time
tsince	Time since the last spike was emitted (from baseSpikeSource)	time

Event Ports

spike

Port on which spikes are emitted (from baseSpikeSource)

Direction: out

Dynamics

State Variables: tsince: time (exposed as tsince); tnext: time (exposed as tnext); isi: time (exposed as isi)
On Start: tsince = 0; isi = minISI + MSEC * random((maxISI - minISI) / MSEC); tnext = isi
On Conditions: IF t > tnext THEN; isi = minISI + MSEC * random((maxISI - minISI) / MSEC); tsince = 0; tnext = tnext+isi; EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1; d tnext /dt = 0

Schema

<xs:complexType name="SpikeGeneratorRandom">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="maxISI" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="minISI" type="Nml2Quantity_time" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SpikeGeneratorRandom
from neuroml.utils import component_factory

variable = component_factory(
    SpikeGeneratorRandom,
    id: 'a NonNegativeInteger (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    max_isi: 'a Nml2Quantity_time (required)' = None,
    min_isi: 'a Nml2Quantity_time (required)' = None,
)

Usage: XML

<spikeGeneratorRandom id="spikeGenRandom" minISI="10 ms" maxISI="30 ms"/>

spikeGeneratorPoisson#

extends baseSpikeSource

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

Parameters

averageRate

The average rate at which spikes are emitted

per_time

Constants

SMALL_TIME = 1e-9ms

time

Exposures

isi	The interval until the next spike	time
tnextIdeal	This is the ideal/perfect next spike time, based on a newly generated isi, but dt precision will mean that it’s usually slightly later than this	time
tnextUsed	This is the next spike time for practical purposes, ensuring that it’s later than the current time	time
tsince	Time since the last spike was emitted (from baseSpikeSource)	time

Event Ports

spike

Port on which spikes are emitted (from baseSpikeSource)

Direction: out

Dynamics

State Variables: tsince: time (exposed as tsince); tnextIdeal: time (exposed as tnextIdeal); tnextUsed: time (exposed as tnextUsed); isi: time (exposed as isi)
On Start: tsince = 0; isi = -1 * log(random(1)) / averageRate; tnextIdeal = isi; tnextUsed = isi
On Conditions: IF t > tnextUsed THEN; tsince = 0; isi = -1 * log(random(1)) / averageRate; tnextIdeal = (tnextIdeal+isi); tnextUsed = tnextIdeal*H( (tnextIdeal-t)/t ) + (t+SMALL_TIME)*H( (t-tnextIdeal)/t ); EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1; d tnextUsed /dt = 0; d tnextIdeal /dt = 0

Schema

<xs:complexType name="SpikeGeneratorPoisson">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="averageRate" type="Nml2Quantity_pertime" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SpikeGeneratorPoisson
from neuroml.utils import component_factory

variable = component_factory(
    SpikeGeneratorPoisson,
    id: 'a NonNegativeInteger (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    average_rate: 'a Nml2Quantity_pertime (required)' = None,
    extensiontype_=None,
)

Usage: XML

<spikeGeneratorPoisson id="spikeGenPoisson" averageRate="50 Hz"/>

spikeGeneratorRefPoisson#

extends spikeGeneratorPoisson

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

Parameters

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

Derived parameters

averageIsi

The average interspike interval

time

averageIsi = 1 / averageRate

Exposures

isi	The interval until the next spike (from spikeGeneratorPoisson)	time
tnextIdeal	This is the ideal/perfect next spike time, based on a newly generated isi, but dt precision will mean that it’s usually slightly later than this (from spikeGeneratorPoisson)	time
tnextUsed	This is the next spike time for practical purposes, ensuring that it’s later than the current time (from spikeGeneratorPoisson)	time
tsince	Time since the last spike was emitted (from baseSpikeSource)	time

Event Ports

spike

Port on which spikes are emitted (from baseSpikeSource)

Direction: out

Dynamics

State Variables: tsince: time (exposed as tsince); tnextIdeal: time (exposed as tnextIdeal); tnextUsed: time (exposed as tnextUsed); isi: time (exposed as isi)
On Start: tsince = 0; isi = minimumISI - (averageIsi-minimumISI) * log(random(1)); tnextIdeal = isi; tnextUsed = isi
On Conditions: IF t > tnextUsed THEN; tsince = 0; isi = minimumISI - (averageIsi-minimumISI) * log(random(1)); tnextIdeal = (tnextIdeal+isi); tnextUsed = tnextIdeal*H( (tnextIdeal-t)/t ) + (t+SMALL_TIME)*H( (t-tnextIdeal)/t ); EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1; d tnextUsed /dt = 0; d tnextIdeal /dt = 0

Schema

<xs:complexType name="SpikeGeneratorRefPoisson">
  <xs:complexContent>
    <xs:extension base="SpikeGeneratorPoisson">
      <xs:attribute name="minimumISI" type="Nml2Quantity_time" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SpikeGeneratorRefPoisson
from neuroml.utils import component_factory

variable = component_factory(
    SpikeGeneratorRefPoisson,
    id: 'a NonNegativeInteger (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    average_rate: 'a Nml2Quantity_pertime (required)' = None,
    minimum_isi: 'a Nml2Quantity_time (required)' = None,
)

Usage: XML

<spikeGeneratorRefPoisson id="spikeGenRefPoisson" averageRate="50 Hz" minimumISI="10 ms"/>

spikeArray#

extends baseSpikeSource

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

Children list

spikes

spike

Exposures

tsince

Time since the last spike was emitted (from baseSpikeSource)

time

Event Ports

in	This will receive events from the children	Direction: in
spike	Port on which spikes are emitted (from baseSpikeSource)	Direction: out

Dynamics

State Variables: tsince: time (exposed as tsince)
On Start: tsince = 0
On Events: EVENT IN on port: in; tsince = 0; EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1

Schema

<xs:complexType name="SpikeArray">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:sequence>
        <xs:element name="spike" type="Spike" minOccurs="0" maxOccurs="unbounded"/>
      </xs:sequence>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SpikeArray
from neuroml.utils import component_factory

variable = component_factory(
    SpikeArray,
    id: 'a NonNegativeInteger (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    spikes: 'list of Spike(s) (optional)' = None,
)

Usage: XML

<spikeArray id="spkArr">
    <spike id="0" time="50 ms"/>
    <spike id="1" time="100 ms"/>
    <spike id="2" time="150 ms"/>
    <spike id="3" time="155 ms"/>
    <spike id="4" time="250 ms"/>
</spikeArray>

spike#

extends baseSpikeSource

Emits a single spike at the specified time.

Parameters

time

Time at which to emit one spike event

time

Exposures

spiked	0 signals not yet spiked, 1 signals has spiked	Dimensionless
tsince	Time since the last spike was emitted (from baseSpikeSource)	time

Event Ports

spike

Port on which spikes are emitted (from baseSpikeSource)

Direction: out

Dynamics

Structure: WITH this AS a; WITH parent AS b; EVENT CONNECTION from a TO b
State Variables: tsince: time (exposed as tsince); spiked: Dimensionless (exposed as spiked)
On Start: tsince = 0
On Conditions: IF (t >= time) AND (spiked = 0) THEN; spiked = 1; tsince = 0; EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1

Schema

<xs:complexType name="Spike">
  <xs:complexContent>
    <xs:extension base="BaseNonNegativeIntegerId">
      <xs:attribute name="time" type="Nml2Quantity_time" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import Spike
from neuroml.utils import component_factory

variable = component_factory(
    Spike,
    id: 'a NonNegativeInteger (required)' = None,
    time: 'a Nml2Quantity_time (required)' = None,
)

Usage: XML

<spike id="0" time="50 ms"/>

<spike id="1" time="100 ms"/>

<spike id="2" time="150 ms"/>

pulseGenerator#

extends basePointCurrent

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

Parameters

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

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Event Ports

in

Note: this is not used here. Will be removed in future

Direction: in

Dynamics

State Variables: i: current (exposed as i)
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; i = 0; IF t >= delay AND t < duration + delay THEN; i = weight * amplitude; IF t >= duration + delay THEN; i = 0

Schema

<xs:complexType name="PulseGenerator">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="amplitude" type="Nml2Quantity_current" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import PulseGenerator
from neuroml.utils import component_factory

variable = component_factory(
    PulseGenerator,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    amplitude: 'a Nml2Quantity_current (required)' = None,
)

Usage: XML

<pulseGenerator id="pulseGen1" delay="50ms" duration="200ms" amplitude="0.0032nA"/>

<pulseGenerator id="pulseGen2" delay="400ms" duration="200ms" amplitude="0.0020nA"/>

<pulseGenerator id="pulseGen3" delay="700ms" duration="200ms" amplitude="0.0010nA"/>

compoundInput#

extends basePointCurrent

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.

Children list

currents

basePointCurrent

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Event Ports

in

Note this is not used here. Will be removed in future

Direction: in

Dynamics

On Events: EVENT IN on port: in
Derived Variables: i_total = currents[*]->i(reduce method: add); i = weight * i_total (exposed as i)

Schema

<xs:complexType name="CompoundInput">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:sequence>
        <xs:element name="pulseGenerator" type="PulseGenerator" minOccurs="0" maxOccurs="unbounded"/>
        <xs:element name="sineGenerator" type="SineGenerator" minOccurs="0" maxOccurs="unbounded"/>
        <xs:element name="rampGenerator" type="RampGenerator" minOccurs="0" maxOccurs="unbounded"/>
      </xs:sequence>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import CompoundInput
from neuroml.utils import component_factory

variable = component_factory(
    CompoundInput,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    pulse_generators: 'list of PulseGenerator(s) (optional)' = None,
    sine_generators: 'list of SineGenerator(s) (optional)' = None,
    ramp_generators: 'list of RampGenerator(s) (optional)' = None,
)

Usage: XML

<compoundInput id="ci0">
    <pulseGenerator id="pg1" delay="50ms" duration="200ms" amplitude=".8 nA"/>
    <pulseGenerator id="pg2" delay="100ms" duration="100ms" amplitude=".4 nA"/>
    <sineGenerator id="sg0" phase="0" delay="125ms" duration="50ms" amplitude=".4nA" period="25ms"/>
</compoundInput>

compoundInputDL#

extends basePointCurrentDL

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.

Children list

currents

basePointCurrentDL

Properties

weight (default: 1)

Dimensionless

Exposures

I

The total (time varying) current produced by this ComponentType (from basePointCurrentDL)

Dimensionless

Event Ports

in

Note this is not used here. Will be removed in future

Direction: in

Dynamics

On Events: EVENT IN on port: in
Derived Variables: I_total = currents[*]->I(reduce method: add); I = weight * I_total (exposed as I)

Schema

<xs:complexType name="CompoundInputDL">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:sequence>
        <xs:element name="pulseGeneratorDL" type="PulseGeneratorDL" minOccurs="0" maxOccurs="unbounded"/>
        <xs:element name="sineGeneratorDL" type="SineGeneratorDL" minOccurs="0" maxOccurs="unbounded"/>
        <xs:element name="rampGeneratorDL" type="RampGeneratorDL" minOccurs="0" maxOccurs="unbounded"/>
      </xs:sequence>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import CompoundInputDL
from neuroml.utils import component_factory

variable = component_factory(
    CompoundInputDL,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    pulse_generator_dls: 'list of PulseGeneratorDL(s) (optional)' = None,
    sine_generator_dls: 'list of SineGeneratorDL(s) (optional)' = None,
    ramp_generator_dls: 'list of RampGeneratorDL(s) (optional)' = None,
)

pulseGeneratorDL#

extends basePointCurrentDL

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

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

Properties

weight (default: 1)

Dimensionless

Exposures

I

The total (time varying) current produced by this ComponentType (from basePointCurrentDL)

Dimensionless

Event Ports

in

Note this is not used here. Will be removed in future

Direction: in

Dynamics

State Variables: I: Dimensionless (exposed as I)
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; I = 0; IF t >= delay AND t < duration + delay THEN; I = weight * amplitude; IF t >= duration + delay THEN; I = 0

Schema

<xs:complexType name="PulseGeneratorDL">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="amplitude" type="Nml2Quantity_none" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import PulseGeneratorDL
from neuroml.utils import component_factory

variable = component_factory(
    PulseGeneratorDL,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    amplitude: 'a Nml2Quantity_current (required)' = None,
)

sineGenerator#

extends basePointCurrent

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

amplitude	Maximum amplitude of current	current
delay	Delay before change in current. Current is zero prior to this.	time
duration	Duration for holding current at amplitude. Current is zero after delay + duration.	time
period	Time period of oscillation	time
phase	Phase (between 0 and 2*pi) at which to start the varying current (i.e. at time given by delay)	Dimensionless

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Event Ports

in

Direction: in

Dynamics

State Variables: i: current (exposed as i)
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; i = 0; IF t >= delay AND t < duration+delay THEN; i = weight * amplitude * sin(phase + (2 * 3.14159265 * (t-delay)/period) ); IF t >= duration+delay THEN; i = 0

Schema

<xs:complexType name="SineGenerator">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="phase" type="Nml2Quantity_none" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="amplitude" type="Nml2Quantity_current" use="required"/>
      <xs:attribute name="period" type="Nml2Quantity_time" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SineGenerator
from neuroml.utils import component_factory

variable = component_factory(
    SineGenerator,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    phase: 'a Nml2Quantity_none (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    amplitude: 'a Nml2Quantity_current (required)' = None,
    period: 'a Nml2Quantity_time (required)' = None,
)

Usage: XML

<sineGenerator id="sg0" phase="0" delay="50ms" duration="200ms" amplitude="1.4nA" period="50ms"/>

<sineGenerator id="sg0" phase="0" delay="125ms" duration="50ms" amplitude=".4nA" period="25ms"/>

sineGeneratorDL#

extends basePointCurrentDL

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

amplitude	Maximum amplitude of current	Dimensionless
delay	Delay before change in current. Current is zero prior to this.	time
duration	Duration for holding current at amplitude. Current is zero after delay + duration.	time
period	Time period of oscillation	time
phase	Phase (between 0 and 2*pi) at which to start the varying current (i.e. at time given by delay)	Dimensionless

Properties

weight (default: 1)

Dimensionless

Exposures

I

The total (time varying) current produced by this ComponentType (from basePointCurrentDL)

Dimensionless

Event Ports

in

Direction: in

Dynamics

State Variables: I: Dimensionless (exposed as I)
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; I = 0; IF t >= delay AND t < duration+delay THEN; I = weight * amplitude * sin(phase + (2 * 3.14159265 * (t-delay)/period) ); IF t >= duration+delay THEN; I = 0

Schema

<xs:complexType name="SineGeneratorDL">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="phase" type="Nml2Quantity_none" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="amplitude" type="Nml2Quantity_none" use="required"/>
      <xs:attribute name="period" type="Nml2Quantity_time" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import SineGeneratorDL
from neuroml.utils import component_factory

variable = component_factory(
    SineGeneratorDL,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    phase: 'a Nml2Quantity_none (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    amplitude: 'a Nml2Quantity_current (required)' = None,
    period: 'a Nml2Quantity_time (required)' = None,
)

rampGenerator#

extends basePointCurrent

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

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

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Event Ports

in

Direction: in

Dynamics

State Variables: i: current (exposed as i)
On Start: i = baselineAmplitude
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; i = weight * baselineAmplitude; IF t >= delay AND t < duration+delay THEN; i = weight * (startAmplitude + (finishAmplitude - startAmplitude) * (t - delay) / (duration)); IF t >= duration+delay THEN; i = weight * baselineAmplitude

Schema

<xs:complexType name="RampGenerator">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="startAmplitude" type="Nml2Quantity_current" use="required"/>
      <xs:attribute name="finishAmplitude" type="Nml2Quantity_current" use="required"/>
      <xs:attribute name="baselineAmplitude" type="Nml2Quantity_current" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import RampGenerator
from neuroml.utils import component_factory

variable = component_factory(
    RampGenerator,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    start_amplitude: 'a Nml2Quantity_current (required)' = None,
    finish_amplitude: 'a Nml2Quantity_current (required)' = None,
    baseline_amplitude: 'a Nml2Quantity_current (required)' = None,
)

Usage: XML

<rampGenerator id="rg0" delay="50ms" duration="200ms" startAmplitude="0.5nA" finishAmplitude="4nA" baselineAmplitude="0nA"/>

rampGeneratorDL#

extends basePointCurrentDL

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

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

Properties

weight (default: 1)

Dimensionless

Exposures

I

The total (time varying) current produced by this ComponentType (from basePointCurrentDL)

Dimensionless

Event Ports

in

Direction: in

Dynamics

State Variables: I: Dimensionless (exposed as I)
On Start: I = baselineAmplitude
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; I = weight * baselineAmplitude; IF t >= delay AND t < duration+delay THEN; I = weight * (startAmplitude + (finishAmplitude - startAmplitude) * (t - delay) / (duration)); IF t >= duration+delay THEN; I = weight * baselineAmplitude

Schema

<xs:complexType name="RampGeneratorDL">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="startAmplitude" type="Nml2Quantity_none" use="required"/>
      <xs:attribute name="finishAmplitude" type="Nml2Quantity_none" use="required"/>
      <xs:attribute name="baselineAmplitude" type="Nml2Quantity_none" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import RampGeneratorDL
from neuroml.utils import component_factory

variable = component_factory(
    RampGeneratorDL,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    start_amplitude: 'a Nml2Quantity_current (required)' = None,
    finish_amplitude: 'a Nml2Quantity_current (required)' = None,
    baseline_amplitude: 'a Nml2Quantity_current (required)' = None,
)

voltageClamp#

extends baseVoltageDepPointCurrent

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

Parameters

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

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepPointCurrent)

voltage

Event Ports

in

Note this is not used here. Will be removed in future

Direction: in

Dynamics

State Variables: i: current (exposed as i)
On Events: EVENT IN on port: in
On Conditions: IF t < delay THEN; i = 0; IF t >= delay THEN; i = weight * (targetVoltage - v) / simpleSeriesResistance; IF t > duration + delay THEN; i = 0

Schema

<xs:complexType name="VoltageClamp">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="targetVoltage" type="Nml2Quantity_voltage" use="required"/>
      <xs:attribute name="simpleSeriesResistance" type="Nml2Quantity_resistance" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import VoltageClamp
from neuroml.utils import component_factory

variable = component_factory(
    VoltageClamp,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    target_voltage: 'a Nml2Quantity_voltage (required)' = None,
    simple_series_resistance: 'a Nml2Quantity_resistance (required)' = None,
)

voltageClampTriple#

extends baseVoltageDepPointCurrent

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	Whether the voltage clamp is active (1) or inactive (0).	Dimensionless
conditioningVoltage	Target voltage before time delay	voltage
delay	Delay before switching from conditioningVoltage to testingVoltage.	time
duration	Duration to hold at testingVoltage.	time
returnVoltage	Target voltage after time duration	voltage
simpleSeriesResistance	Current will be calculated by the difference in voltage between the target and parent, divided by this value	resistance
testingVoltage	Target voltage between times delay and delay + duration	voltage

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepPointCurrent)

voltage

Event Ports

in

Note this is not used here. Will be removed in future

Direction: in

Dynamics

State Variables: i: current (exposed as i)
On Events: EVENT IN on port: in
On Conditions: IF active = 1 AND t < delay THEN; i = weight * (conditioningVoltage - v) / simpleSeriesResistance; IF active = 1 AND t >= delay THEN; i = weight * (testingVoltage - v) / simpleSeriesResistance; IF active = 1 AND t > duration + delay THEN; i = weight * (returnVoltage - v) / simpleSeriesResistance

Schema

<xs:complexType name="VoltageClampTriple">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="active" type="ZeroOrOne" use="required"/>
      <xs:attribute name="delay" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="conditioningVoltage" type="Nml2Quantity_voltage" use="required"/>
      <xs:attribute name="testingVoltage" type="Nml2Quantity_voltage" use="required"/>
      <xs:attribute name="returnVoltage" type="Nml2Quantity_voltage" use="required"/>
      <xs:attribute name="simpleSeriesResistance" type="Nml2Quantity_resistance" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

Go to the libNeuroML documentation

from neuroml import VoltageClampTriple
from neuroml.utils import component_factory

variable = component_factory(
    VoltageClampTriple,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    active: 'a ZeroOrOne (required)' = None,
    delay: 'a Nml2Quantity_time (required)' = None,
    duration: 'a Nml2Quantity_time (required)' = None,
    conditioning_voltage: 'a Nml2Quantity_voltage (required)' = None,
    testing_voltage: 'a Nml2Quantity_voltage (required)' = None,
    return_voltage: 'a Nml2Quantity_voltage (required)' = None,
    simple_series_resistance: 'a Nml2Quantity_resistance (required)' = None,
)

Usage: XML

<voltageClampTriple id="vClamp0" active="1" delay="50ms" duration="200ms" conditioningVoltage="-70mV" testingVoltage="-50mV" returnVoltage="-70mV" simpleSeriesResistance="1e6ohm"/>

Inputs

Contents

Inputs#

basePointCurrent#

baseVoltageDepPointCurrent#

baseVoltageDepPointCurrentSpiking#

basePointCurrentDL#

baseVoltageDepPointCurrentDL#

baseSpikeSource#

spikeGenerator#

spikeGeneratorRandom#

spikeGeneratorPoisson#

spikeGeneratorRefPoisson#

poissonFiringSynapse#

transientPoissonFiringSynapse#

timedSynapticInput#

spikeArray#

spike#

pulseGenerator#

compoundInput#

compoundInputDL#

pulseGeneratorDL#

sineGenerator#

sineGeneratorDL#

rampGenerator#

rampGeneratorDL#

voltageClamp#

voltageClampTriple#