Human-L2-3-Cortical-Microcircuit/NeuroML2/synapses/Gfluct.nml

<?xml version="1.0" encoding="ISO-8859-1"?>
<neuroml xmlns="http://www.neuroml.org/schema/neuroml2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2.3.xsd" id="GfluctDoc">

    <!-- Note that order of different LEMS types matters for validation, since the schema defines what order they should appear in -->

    <!--
        Fluctuating conductance model for synaptic bombardment
        ======================================================

        THEORY

          Synaptic bombardment is represented by a stochastic model containing
          two fluctuating conductances g_e(t) and g_i(t) descibed by:

             Isyn = g_e(t) * [V - E_e] + g_i(t) * [V - E_i]
             d g_e / dt = -(g_e - g_e0) / tau_e + sqrt(D_e) * Ft
             d g_i / dt = -(g_i - g_i0) / tau_i + sqrt(D_i) * Ft

          where E_e, E_i are the reversal potentials, g_e0, g_i0 are the average
          conductances, tau_e, tau_i are time constants, D_e, D_i are noise diffusion
          coefficients and Ft is a gaussian white noise of unit standard deviation.

          g_e and g_i are described by an Ornstein-Uhlenbeck (OU) stochastic process
          where tau_e and tau_i represent the "correlation" (if tau_e and tau_i are
          zero, g_e and g_i are white noise).  The estimation of OU parameters can
          be made from the power spectrum:

             S(w) =  2 * D * tau^2 / (1 + w^2 * tau^2)

          and the diffusion coeffient D is estimated from the variance:

             D = 2 * sigma^2 / tau


        NUMERICAL RESOLUTION

          The numerical scheme for integration of OU processes takes advantage
          of the fact that these processes are gaussian, which led to an exact
          update rule independent of the time step dt (see Gillespie DT, Am J Phys
          64: 225, 1996):

             x(t+dt) = x(t) * exp(-dt/tau) + A * N(0,1)

          where A = sqrt( D*tau/2 * (1-exp(-2*dt/tau)) ) and N(0,1) is a normal
          random number (avg=0, sigma=1)


        IMPLEMENTATION

          This mechanism is implemented as a nonspecific current defined as a
          point process.


        PARAMETERS

          The mechanism takes the following parameters:

             E_e = 0  (mV)      : reversal potential of excitatory conductance
             E_i = -75 (mV)     : reversal potential of inhibitory conductance

             g_e0 = 0.0121 (umho)   : average excitatory conductance
             g_i0 = 0.0573 (umho)   : average inhibitory conductance

             std_e = 0.0030 (umho)  : standard dev of excitatory conductance
             std_i = 0.0066 (umho)  : standard dev of inhibitory conductance

             tau_e = 2.728 (ms)     : time constant of excitatory conductance
             tau_i = 10.49 (ms)     : time constant of inhibitory conductance


        Gfluct2: conductance cannot be negative


        REFERENCE

          Destexhe, A., Rudolph, M., Fellous, J-M. and Sejnowski, T.J.
          Fluctuating synaptic conductances recreate in-vivo-like activity in
          neocortical neurons. Neuroscience 107: 13-24 (2001).

          (electronic copy available at http://cns.iaf.cnrs-gif.fr)


          A. Destexhe, 1999

        Reference:
        https://doi.org/10.1016/S0306-4522(01)00344-X

        Original mod source: https://modeldb.science/8115
    -->

    <ComponentType name="Gfluct"
        extends="baseVoltageDepPointCurrent"
        description="Fluctuating conductance model for synaptic bombardment: Destexhe et al 2001"
        >

        <Parameter name="start" dimension="time" description="start time"/>
        <Parameter name="stop" dimension="time" description="stop time"/>

        <Parameter name="dt" dimension="time" description="simulation time step"/>
        <Parameter name="E_e" dimension="voltage" description="Excitatory conductance reversal potential"/>
        <Parameter name="E_i" dimension="voltage" description="Inhibitory conductance reversal potential"/>

        <Parameter name="g_e0" dimension="conductance" description="Average excitatory conductance"/>
        <Parameter name="g_i0" dimension="conductance" description="Average inhibitory conductance"/>

        <Parameter name="std_e" dimension="conductance" description="Std dev of excitatory conductance"/>
        <Parameter name="std_i" dimension="conductance" description="Std dev of inhibitory conductance"/>

        <Parameter name="tau_e" dimension="time" description="Time constant of excitatory conductance"/>
        <Parameter name="tau_i" dimension="time" description="Time constant of inhibitory conductance"/>

        <Constant name="zeroms" value="0 ms" dimension="time" />

        <EventPort name="in" direction="in" description="Note this is not used here. Will be removed in future"/>

        <DerivedParameter name="exp_e" dimension="none" value="exp(-dt/tau_e)" />
        <DerivedParameter name="amp_e" dimension="conductance" value="std_e * sqrt((1 - exp(-2 * (dt/tau_e))))" />
        <DerivedParameter name="exp_i" dimension="none" value="exp(-dt/tau_i)" />
        <DerivedParameter name="amp_i" dimension="conductance" value="std_i * sqrt((1 - exp(-2 * (dt/tau_i))))" />

        <Exposure name="g_e" dimension="conductance" />
        <Exposure name="g_i" dimension="conductance" />
        <Exposure name="g_e1" dimension="conductance" />
        <Exposure name="g_i1" dimension="conductance" />
        <Exposure name="i" dimension="current" />

        <Dynamics>
            <!-- total conductances -->
            <StateVariable name="g_e" exposure="g_e" dimension="conductance" />
            <StateVariable name="g_i" exposure="g_i" dimension="conductance" />
            <!-- fluctuating conductances -->
            <StateVariable name="g_e1" exposure="g_e1" dimension="conductance" />
            <StateVariable name="g_i1" exposure="g_i1" dimension="conductance" />

            <!-- track old value of g_e1: LEMS doesn't seem to like it if the same variable is used -->
            <StateVariable name="g_e1_old" dimension="conductance" />
            <StateVariable name="g_i1_old" dimension="conductance" />

            <StateVariable name="i" exposure="i" dimension="current" />

            <StateVariable name="r1" dimension="none" />
            <StateVariable name="r2" dimension="none" />
            <StateVariable name="randn" dimension="none" />

            <OnEvent port="in"><!--TODO: remove, see above...
            <StateAssignment variable="i" value="0"/>-->
            </OnEvent>

            <!-- required ? -->
            <OnStart>
                <StateAssignment variable="i" value="0"/>
                <StateAssignment variable="g_e" value="0"/>
                <StateAssignment variable="g_i" value="0"/>
                <StateAssignment variable="g_e1" value="0" />
                <StateAssignment variable="g_e1_old" value="0" />
                <StateAssignment variable="g_i1_old" value="0" />
            </OnStart>

            <!-- before start -->
            <OnCondition test="t .lt. start">
                <StateAssignment variable="i" value="0"/>
                <StateAssignment variable="g_e" value="0"/>
                <StateAssignment variable="g_i" value="0"/>
                <StateAssignment variable="g_e1" value="0" />
                <StateAssignment variable="g_i1" value="0" />
                <StateAssignment variable="g_e1_old" value="0" />
                <StateAssignment variable="g_i1_old" value="0" />
            </OnCondition>

            <!-- generate gaussian random number -->
            <OnCondition test="t .geq. start .and. t.lt. stop">
                <StateAssignment variable="r1" value="random(1)"/>
                <StateAssignment variable="r2" value="random(1)"/>
                <StateAssignment variable="randn" value="sqrt(-2*log(r1))*cos(2*3.14159265359*r2)"/>
            </OnCondition>

            <!-- oup()-->
            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_e .neq. zeroms)" >
                <StateAssignment variable="g_e1" value="(exp_e * g_e1_old) + (amp_e * randn)" />
                <StateAssignment variable="g_e1_old" value="g_e1" />
            </OnCondition>

            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_i .neq. zeroms)" >
                <StateAssignment variable="g_i1" value="(exp_i * g_i1_old) + (amp_i * randn)" />
                <StateAssignment variable="g_i1_old" value="g_i1" />
            </OnCondition>

            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_e .eq. zeroms)" >
                <StateAssignment variable="g_e1" value="std_e * randn" />
            </OnCondition>
            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_i .eq. zeroms)" >
                <StateAssignment variable="g_i1" value="std_i * randn" />
            </OnCondition>

            <OnCondition test="t .geq. start .and. t .lt. stop">
                <StateAssignment variable="g_e" value="g_e0 + g_e1" />
                <StateAssignment variable="g_i" value="g_i0 + g_i1" />
            </OnCondition>

            <OnCondition test="g_e .lt. 0">
                <StateAssignment variable="g_e" value="0" />
            </OnCondition>
            <OnCondition test="g_i .lt. 0">
                <StateAssignment variable="g_i" value="0" />
            </OnCondition>

            <OnCondition test="t .geq. start .and. t .lt. stop">
                <StateAssignment variable="i" value="-1 * g_e * (v - E_e) - g_i * (v - E_i)" />
            </OnCondition>

            <!-- after end -->
            <OnCondition test="t .geq. stop">
                <StateAssignment variable="i" value="0"/>
                <StateAssignment variable="g_e" value="0"/>
                <StateAssignment variable="g_i" value="0"/>
                <StateAssignment variable="g_e1" value="0" />
                <StateAssignment variable="g_i1" value="0" />
            </OnCondition>
        </Dynamics>

    </ComponentType>

</neuroml>