Synapses

A number of synaptic ComponentTypes for use in NeuroML 2 documents, e.g. expOneSynapse, expTwoSynapse, blockingPlasticSynapse. These extend the baseSynapse ComponentType. Also defined continuously transmitting synapses, e.g. gapJunction and gradedSynapse.


Original ComponentType definitions: Synapses.xml. Schema against which NeuroML based on these should be valid: NeuroML_v2.2.xsd. Generated on 25/08/21 from this commit. Please file any issues or questions at the issue tracker here.


baseSynapse

extends basePointCurrent

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

Bioportal entry for Computational Neuroscience Ontology related to baseSynapse.

i

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

current

in

Direction: in

Go to the libNeuroML documentation

from neuroml import BaseSynapse

variable = BaseSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, **kwargs_)

baseVoltageDepSynapse

extends baseSynapse

Base type for synapses with a dependence on membrane potential.

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

Go to the libNeuroML documentation

from neuroml import BaseVoltageDepSynapse

variable = BaseVoltageDepSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, **kwargs_)

baseSynapseDL

extends baseVoltageDepPointCurrentDL

Base type for all synapses, i.e. ComponentTypes which produce a dimensionless current and change Dynamics in response to an incoming event.

Bioportal entry for Computational Neuroscience Ontology related to baseSynapseDL.

I

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

Dimensionless

V

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

Dimensionless

baseCurrentBasedSynapse

extends baseSynapse

Synapse model which produces a synaptic current.

i

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

current

in

(from baseSynapse)

Direction: in

Go to the libNeuroML documentation

from neuroml import BaseCurrentBasedSynapse

variable = BaseCurrentBasedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, extensiontype_=None, **kwargs_)

alphaCurrentSynapse

extends baseCurrentBasedSynapse

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

ibase

Baseline current increase after receiving a spike

current

tau

Time course for rise and decay

time

weight (default: 1)

Dimensionless

i

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

current

in

(from baseSynapse)

Direction: in

State Variables

I: current

J: current

On Start

I = 0

J = 0

On Events

EVENT IN on port: in

   J = J + weight * ibase

Derived Variables

i = I (exposed as i)

Time Derivatives

d I /dt = (2.7182818284590451*J - I)/tau

d J /dt = -J/tau

Go to the libNeuroML documentation

from neuroml import AlphaCurrentSynapse

variable = AlphaCurrentSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau=None, ibase=None, **kwargs_)

baseConductanceBasedSynapse

extends baseVoltageDepSynapse

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

Bioportal entry for Computational Neuroscience Ontology related to baseConductanceBasedSynapse.

erev

Reversal potential of the synapse

voltage

gbase

Baseline conductance, generally the maximum conductance following a single spike

conductance

g

Time varying conductance through the synapse

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

Go to the libNeuroML documentation

from neuroml import BaseConductanceBasedSynapse

variable = BaseConductanceBasedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, extensiontype_=None, **kwargs_)

baseConductanceBasedSynapseTwo

extends baseVoltageDepSynapse

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

Bioportal entry for Computational Neuroscience Ontology related to baseConductanceBasedSynapseTwo.

erev

Reversal potential of the synapse

voltage

gbase1

Baseline conductance 1

conductance

gbase2

Baseline conductance 2

conductance

g

Time varying conductance through the synapse

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

Go to the libNeuroML documentation

from neuroml import BaseConductanceBasedSynapseTwo

variable = BaseConductanceBasedSynapseTwo(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase1=None, gbase2=None, erev=None, extensiontype_=None, **kwargs_)

expOneSynapse

extends baseConductanceBasedSynapse

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

erev

Reversal potential of the synapse (from baseConductanceBasedSynapse)

voltage

gbase

Baseline conductance, generally the maximum conductance following a single spike (from baseConductanceBasedSynapse)

conductance

tauDecay

Time course of decay

time

weight (default: 1)

Dimensionless

g

Time varying conductance through the synapse (from baseConductanceBasedSynapse)

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

State Variables

g: conductance  (exposed as g)

On Start

g = 0

On Events

EVENT IN on port: in

   g = g + (weight * gbase)

Derived Variables

i = g * (erev - v) (exposed as i)

Time Derivatives

d g /dt = -g / tauDecay

Go to the libNeuroML documentation

from neuroml import ExpOneSynapse

variable = ExpOneSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, tau_decay=None, **kwargs_)
<expOneSynapse id="syn1" gbase="5nS" erev="0mV" tauDecay="3ms"/>
<expOneSynapse id="syn2" gbase="10nS" erev="0mV" tauDecay="2ms"/>
<expOneSynapse id="syn1" gbase="5nS" erev="0mV" tauDecay="3ms"/>

alphaSynapse

extends baseConductanceBasedSynapse

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..

erev

Reversal potential of the synapse (from baseConductanceBasedSynapse)

voltage

gbase

Baseline conductance, generally the maximum conductance following a single spike (from baseConductanceBasedSynapse)

conductance

tau

Time course of rise/decay

time

weight (default: 1)

Dimensionless

g

Time varying conductance through the synapse (from baseConductanceBasedSynapse)

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

State Variables

g: conductance  (exposed as g)

A: conductance

On Start

g = 0

A = 0

On Events

EVENT IN on port: in

   A = A + (gbase*weight)

Derived Variables

i = g * (erev - v) (exposed as i)

Time Derivatives

d g /dt = (2.7182818284590451 * A - g)/tau

d A /dt = -A / tau

Go to the libNeuroML documentation

from neuroml import AlphaSynapse

variable = AlphaSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, gbase=None, erev=None, tau=None, **kwargs_)
<alphaSynapse id="synalpha" gbase="0.5nS" erev="0mV" tau="2ms">
        <notes>An alpha synapse with time for rise equal to decay.</notes>
    </alphaSynapse>

expTwoSynapse

extends baseConductanceBasedSynapse

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..

erev

Reversal potential of the synapse (from baseConductanceBasedSynapse)

voltage

gbase

Baseline conductance, generally the maximum conductance following a single spike (from baseConductanceBasedSynapse)

conductance

tauDecay

time

tauRise

time

peakTime

time

waveformFactor

Dimensionless

weight (default: 1)

Dimensionless

g

Time varying conductance through the synapse (from baseConductanceBasedSynapse)

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

State Variables

A: Dimensionless

B: Dimensionless

On Start

A = 0

B = 0

On Events

EVENT IN on port: in

   A = A + (weight * waveformFactor)

   B = B + (weight * waveformFactor)

Derived Variables

g = gbase * (B - A) (exposed as g)

i = g * (erev - v) (exposed as i)

Time Derivatives

d A /dt = -A / tauRise

d B /dt = -B / tauDecay

Go to the libNeuroML documentation

from neuroml import ExpTwoSynapse

variable = 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, **kwargs_)
<expTwoSynapse id="AMPA" gbase="0.5nS" erev="0mV" tauRise="1ms" tauDecay="2ms"/>
<expTwoSynapse id="synInput" gbase="8nS" erev="20mV" tauRise="1ms" tauDecay="5ms"/>
<expTwoSynapse id="synInputFast" gbase="1nS" erev="20mV" tauRise="0.2ms" tauDecay="1ms"/>

expThreeSynapse

extends baseConductanceBasedSynapseTwo

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.

erev

Reversal potential of the synapse (from baseConductanceBasedSynapseTwo)

voltage

gbase1

Baseline conductance 1 (from baseConductanceBasedSynapseTwo)

conductance

gbase2

Baseline conductance 2 (from baseConductanceBasedSynapseTwo)

conductance

tauDecay1

time

tauDecay2

time

tauRise

time

peakTime1

time

peakTime2

time

waveformFactor1

Dimensionless

waveformFactor2

Dimensionless

weight (default: 1)

Dimensionless

g

Time varying conductance through the synapse (from baseConductanceBasedSynapseTwo)

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

State Variables

A: Dimensionless

B: Dimensionless

C: Dimensionless

On Start

A = 0

B = 0

C = 0

On Events

EVENT IN on port: in

   A = A + (gbase1weight * waveformFactor1 + gbase2weight*waveformFactor2 )/(gbase1+gbase2)

   B = B + (weight * waveformFactor1)

   C = C + (weight * waveformFactor2)

Derived Variables

g = gbase1*(B - A) + gbase2*(C-A) (exposed as g)

i = g * (erev - v) (exposed as i)

Time Derivatives

d A /dt = -A / tauRise

d B /dt = -B / tauDecay1

d C /dt = -C / tauDecay2

Go to the libNeuroML documentation

from neuroml import ExpThreeSynapse

variable = 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, **kwargs_)
<expThreeSynapse id="synInputFastTwo" gbase1="1.5nS" tauRise="0.1ms" tauDecay1="0.7ms" gbase2="0.5nS" tauDecay2="2.5ms" erev="0mV"/>
<expThreeSynapse id="AMPA" gbase1="1.5nS" tauRise="0.1ms" tauDecay1="0.7ms" gbase2="0.5nS" tauDecay2="2.5ms" erev="0mV">
        <notes>A synapse consisting of one rise and two decay time courses.</notes>
    </expThreeSynapse>

baseBlockMechanism

Base of any ComponentType which produces a varying scaling ( or blockage ) of synaptic strength of magnitude scaling.

blockFactor

Dimensionless

voltageConcDepBlockMechanism

extends baseBlockMechanism

Synaptic blocking mechanism which varys with membrane potential across the synapse, e.g. in NMDA receptor mediated synapses.

blockConcentration

concentration

scalingConc

concentration

scalingVolt

voltage

species

blockFactor

(from baseBlockMechanism)

Dimensionless

Derived Variables

blockFactor = 1/(1 + (blockConcentration / scalingConc)* exp(-1 * (v / scalingVolt))) (exposed as blockFactor)

basePlasticityMechanism

Base plasticity mechanism.

plasticityFactor

Dimensionless

in

This is where the plasticity mechanism receives spike events from the parent synapse.

Direction: in

tsodyksMarkramDepMechanism

extends basePlasticityMechanism

Depression-only Tsodyks-Markram model, as in Tsodyks and Markram 1997.

initReleaseProb

Dimensionless

tauRec

time

plasticityFactor

(from basePlasticityMechanism)

Dimensionless

in

This is where the plasticity mechanism receives spike events from the parent synapse. (from basePlasticityMechanism)

Direction: in

Structure

WITH parent AS a

WITH this AS b

EVENT CONNECTION from a TO b

State Variables

R: Dimensionless

On Start

R = 1

On Events

EVENT IN on port: in

   R = R * (1 - U)

Derived Variables

U = initReleaseProb

plasticityFactor = R * U (exposed as plasticityFactor)

Time Derivatives

d R /dt = (1 - R) / tauRec

tsodyksMarkramDepFacMechanism

extends basePlasticityMechanism

Full Tsodyks-Markram STP model with both depression and facilitation, as in Tsodyks, Pawelzik and Markram 1998.

initReleaseProb

Dimensionless

tauFac

time

tauRec

time

plasticityFactor

(from basePlasticityMechanism)

Dimensionless

in

This is where the plasticity mechanism receives spike events from the parent synapse. (from basePlasticityMechanism)

Direction: in

Structure

WITH parent AS a

WITH this AS b

EVENT CONNECTION from a TO b

State Variables

R: Dimensionless

U: Dimensionless

On Start

R = 1

U = initReleaseProb

On Events

EVENT IN on port: in

   R = R * (1 - U)

   U = U + initReleaseProb * (1 - U)

Derived Variables

plasticityFactor = R * U (exposed as plasticityFactor)

Time Derivatives

d R /dt = (1 - R) / tauRec

d U /dt = (initReleaseProb - U) / tauFac

blockingPlasticSynapse

extends expTwoSynapse

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

erev

Reversal potential of the synapse (from baseConductanceBasedSynapse)

voltage

gbase

Baseline conductance, generally the maximum conductance following a single spike (from baseConductanceBasedSynapse)

conductance

tauDecay

(from expTwoSynapse)

time

tauRise

(from expTwoSynapse)

time

peakTime

(from expTwoSynapse)

time

waveformFactor

(from expTwoSynapse)

Dimensionless

plasticityMechanisms

basePlasticityMechanism

blockMechanisms

baseBlockMechanism

weight (default: 1)

Dimensionless

g

Time varying conductance through the synapse (from baseConductanceBasedSynapse)

conductance

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

relay

Used to relay incoming spikes to child plasticity mechanism

Direction: out

State Variables

A: Dimensionless

B: Dimensionless

On Start

A = 0

B = 0

On Events

EVENT IN on port: in

   A = A + (weight * plasticityFactor * waveformFactor)

   B = B + (weight * plasticityFactor * waveformFactor)

   EVENT OUT on port: relay

Derived Variables

plasticityFactor = plasticityMechanisms[*]->plasticityFactor(reduce method: multiply)

blockFactor = blockMechanisms[*]->blockFactor(reduce method: multiply)

g = blockFactor * gbase * (B - A) (exposed as g)

i = g * (erev - v) (exposed as i)

Time Derivatives

d A /dt = -A / tauRise

d B /dt = -B / tauDecay

Go to the libNeuroML documentation

from neuroml import BlockingPlasticSynapse

variable = 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, **kwargs_)
<blockingPlasticSynapse id="NMDA" gbase=".8nS" tauRise="1e-3s" tauDecay="13.3333e-3s" erev="0V">
        <blockMechanism type="voltageConcDepBlockMechanism" species="mg" blockConcentration="1.2mM" scalingConc="1.9205441817997078mM" scalingVolt="0.016129032258064516V"/>
    </blockingPlasticSynapse>
<blockingPlasticSynapse id="blockStpSynDep" gbase="1nS" erev="0mV" tauRise="0.1ms" tauDecay="2ms">
        <notes>A biexponential blocking synapse, with STD.</notes>
        <plasticityMechanism type="tsodyksMarkramDepMechanism" initReleaseProb="0.5" tauRec="120 ms"/>
        <blockMechanism type="voltageConcDepBlockMechanism" species="mg" blockConcentration="1.2 mM" scalingConc="1.920544 mM" scalingVolt="16.129 mV"/>
    </blockingPlasticSynapse>
<blockingPlasticSynapse id="blockStpSynDepFac" gbase="1nS" erev="0mV" tauRise="0.1ms" tauDecay="2ms">
        <notes>A biexponential blocking synapse with short term
            depression and facilitation.</notes>
        <plasticityMechanism type="tsodyksMarkramDepFacMechanism" initReleaseProb="0.5" tauRec="120 ms" tauFac="10 ms"/>
        <blockMechanism type="voltageConcDepBlockMechanism" species="mg" blockConcentration="1.2 mM" scalingConc="1.920544 mM" scalingVolt="16.129 mV"/>
    </blockingPlasticSynapse>

doubleSynapse

extends baseVoltageDepSynapse

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

synapse1Path

synapse2Path

synapse1

baseSynapse

synapse2

baseSynapse

weight (default: 1)

Dimensionless

i

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

current

v

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

voltage

in

(from baseSynapse)

Direction: in

relay

Used to relay incoming spikes to child mechanisms

Direction: out

Structure

WITH this AS a

WITH synapse1Path AS b

WITH synapse2Path AS c

CHILD INSTANCE: synapse1

CHILD INSTANCE: synapse2

EVENT CONNECTION from a TO c

EVENT CONNECTION from a TO b

State Variables

weightFactor: Dimensionless

On Events

EVENT IN on port: in

   weightFactor = weight

   EVENT OUT on port: relay

Derived Variables

i1 = synapse1->i

i2 = synapse2->i

i = weightFactor * (i1 + i2) (exposed as i)

Go to the libNeuroML documentation

from neuroml import DoubleSynapse

variable = 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, **kwargs_)
<doubleSynapse id="AMPA_NMDA" synapse1="AMPA" synapse1Path="./AMPA" synapse2="NMDA" synapse2Path="./NMDA">
        <notes>A single "synapse" which contains both AMPA and NMDA. It is planned that the need for extra synapse1Path/synapse2Path attributes can be removed in later versions.</notes>
    </doubleSynapse>

stdpSynapse

extends expTwoSynapse

Spike timing dependent plasticity mechanism, NOTE: EXAMPLE NOT YET WORKING!!!

Bioportal entry for Computational Neuroscience Ontology related to stdpSynapse.

erev

Reversal potential of the synapse (from baseConductanceBasedSynapse)

voltage

gbase

Baseline conductance, generally the maximum conductance following a single spike (from baseConductanceBasedSynapse)

conductance

tauDecay

(from expTwoSynapse)

time

tauRise

(from expTwoSynapse)

time

peakTime

(from expTwoSynapse)

time

waveformFactor

(from expTwoSynapse)

Dimensionless

tsinceRate = 1

Dimensionless

longTime = 1000s

time

M

Dimensionless

P

Dimensionless

g

Time varying conductance through the synapse (from baseConductanceBasedSynapse)

conductance

i

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

current

tsince

time

v

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

voltage

in

(from baseSynapse)

Direction: in

State Variables

A: Dimensionless

B: Dimensionless

M: Dimensionless  (exposed as M)

P: Dimensionless  (exposed as P)

tsince: time  (exposed as tsince)

On Start

A = 0

B = 0

M = 1

P = 1

tsince = longTime

On Events

EVENT IN on port: in

   A = A + waveformFactor

   B = B + waveformFactor

   tsince = 0

Derived Variables

g = gbase * (B - A) (exposed as g)

i = g * (erev - v) (exposed as i)

Time Derivatives

d A /dt = -A / tauRise

d B /dt = -B / tauDecay

d tsince /dt = tsinceRate

gapJunction

extends baseSynapse

Gap junction/single electrical connection.

conductance

conductance

weight (default: 1)

Dimensionless

i

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

current

in

(from baseSynapse)

Direction: in

Derived Variables

vpeer = peer->v

i = weight * conductance * (vpeer - v) (exposed as i)

Go to the libNeuroML documentation

from neuroml import GapJunction

variable = GapJunction(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, conductance=None, **kwargs_)
<gapJunction id="gj1" conductance="10pS"/>
<gapJunction id="gj1" conductance="10pS"/>

baseGradedSynapse

extends baseSynapse

Base type for graded synapses.

i

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

current

in

(from baseSynapse)

Direction: in

silentSynapse

extends baseGradedSynapse

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

weight (default: 1)

Dimensionless

i

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

current

in

(from baseSynapse)

Direction: in

Derived Variables

vpeer = peer->v

i = 0 (exposed as i)

Go to the libNeuroML documentation

from neuroml import SilentSynapse

variable = SilentSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, **kwargs_)
<silentSynapse id="silent1"/>
<silentSynapse id="silent2"/>
<silentSynapse id="silent1"/>

linearGradedSynapse

extends baseGradedSynapse

Behaves just like a one way gap junction.

conductance

conductance

weight (default: 1)

Dimensionless

i

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

current

in

(from baseSynapse)

Direction: in

Derived Variables

vpeer = peer->v

i = weight * conductance * (vpeer - v) (exposed as i)

Go to the libNeuroML documentation

from neuroml import LinearGradedSynapse

variable = LinearGradedSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, conductance=None, **kwargs_)
<linearGradedSynapse id="gs1" conductance="5pS"/>

gradedSynapse

extends baseGradedSynapse

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

Vth

The half-activation voltage of the synapse

voltage

conductance

conductance

delta

Slope of the activation curve

voltage

erev

The reversal potential of the synapse

voltage

k

Rate constant for transmitter-receptor dissociation rate

per_time

weight (default: 1)

Dimensionless

i

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

current

inf

Dimensionless

tau

time

in

(from baseSynapse)

Direction: in

State Variables

s: Dimensionless

On Conditions

IF (1-inf) < 1e-4 THEN

   s = inf

Derived Variables

vpeer = peer->v

inf = 1/(1 + exp((Vth - vpeer)/delta)) (exposed as inf)

tau = (1-inf)/k (exposed as tau)

i = weight * conductance * s * (erev-v) (exposed as i)

Conditional Derived Variables

IF (1-inf) > 1e-4 THEN

s_rate = (inf - s)/tau

OTHERWISE

s_rate = 0

Time Derivatives

d s /dt = s_rate

Go to the libNeuroML documentation

from neuroml import GradedSynapse

variable = 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, **kwargs_)
<gradedSynapse id="gs2" conductance="5pS" delta="5mV" Vth="-55mV" k="0.025per_ms" erev="0mV"/>
<gradedSynapse id="gs1" conductance="0.1nS" delta="5mV" Vth="-35mV" k="0.025per_ms" erev="0mV"/>