Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)

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Accession:187474
" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. ... We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ..."
References:
1 . Hay E, Hill S, Schürmann F, Markram H, Segev I (2011) Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol 7:e1002107 [PubMed]
2 . Hay E, Segev I (2015) Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cereb Cortex 25:3561-71 [PubMed]
3 . Mäki-Marttunen T, Halnes G, Devor A, Metzner C, Dale AM, Andreassen OA, Einevoll GT (2018) A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells. J Neurosci Methods 293:264-283 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; NEURON (web link to model); Python; NeuroML;
Model Concept(s):
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi]; Metzner, Christoph [c.metzner at herts.ac.uk];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow;
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TITLE AMPA and NMDA receptor with presynaptic short-term plasticity 


COMMENT
AMPA and NMDA receptor conductance using a dual-exponential profile
presynaptic short-term plasticity based on Fuhrmann et al. 2002
Implemented by Srikanth Ramaswamy, Blue Brain Project, July 2009
Etay: changed weight to be equal for NMDA and AMPA, gmax accessible in Neuron
Tuomo: Changed the data structure so that each instance of this synapse corresponds
       to a group of synapses. The previous activation time and variables Pv and u
       are saved for each "sub-synapse" in this synapse group. Added function
       setVec() which saves the pointer to these data.
Tuomo: In addition to the synapse-wise data, also the past and future activation
       times are saved. This allows more complicated synapse activation time
       distributions than stationary Poissonian activation times (e.g., oscillatory
       Poissonian inputs). Added function setVec2 which sets the vector of IDs of
       synapses that are activated. This way, whenever the synapse group is activated,
       the program checks whose turn (with which ID) it is to fire.
ENDCOMMENT


NEURON {

        POINT_PROCESS ProbAMPANMDA2groupdet
        RANGE tau_r_AMPA, tau_d_AMPA, tau_r_NMDA, tau_d_NMDA, Nsyns, Nevents, eventCounter
        RANGE Use, u, Dep, Fac, u0, weight_factor_NMDA
        RANGE i, i_AMPA, i_NMDA, g_AMPA, g_NMDA, e, gmax
        NONSPECIFIC_CURRENT i, i_AMPA,i_NMDA
	POINTER rng
}

PARAMETER {

        tau_r_AMPA = 0.2   (ms)  : dual-exponential conductance profile
        tau_d_AMPA = 1.7    (ms)  : IMPORTANT: tau_r < tau_d
	tau_r_NMDA = 0.29   (ms) : dual-exponential conductance profile
        tau_d_NMDA = 43     (ms) : IMPORTANT: tau_r < tau_d
        Use = 1.0   (1)   : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values) 
        Dep = 100   (ms)  : relaxation time constant from depression
        Fac = 10   (ms)  :  relaxation time constant from facilitation
        e = 0     (mV)  : AMPA and NMDA reversal potential
	mg = 1   (mM)  : initial concentration of mg2+
        mggate
    	gmax = .001 (uS) : weight conversion factor (from nS to uS)
    	u0 = 0 :initial value of u, which is the running value of Use
	Nsyns = 10 : How many synapses are there actually (the size of "space" divided by three)
	Nevents = 0 : How many events will there be (the size of "space2")
        weight_factor_NMDA = 1
}

COMMENT
The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1 
for comparison with Pr to decide whether to activate the synapse or not
ENDCOMMENT
   
VERBATIM

#include<stdlib.h>
#include<stdio.h>
#include<math.h>

double nrn_random_pick(void* r);
void* nrn_random_arg(int argpos);

extern int ifarg(int iarg);
extern int vector_capacity(void* vv);
extern void* vector_arg(int iarg);
ENDVERBATIM
  

ASSIGNED {

        v (mV)
        i (nA)
	i_AMPA (nA)
	i_NMDA (nA)
        g_AMPA (uS)
	g_NMDA (uS)
        factor_AMPA
	factor_NMDA
	rng
 	space        : A pointer to the vector containing the synapse times. Note that the underlying vector should not be touched after initialization by setVec().
 	space2       : A pointer to the vector containing the event IDs. Note that the underlying vector should not be touched after initialization by setVec2().
        eventCounter : An index for space2 (counts the passed events)
}

STATE {

        A_AMPA       : AMPA state variable to construct the dual-exponential profile - decays with conductance tau_r_AMPA
        B_AMPA       : AMPA state variable to construct the dual-exponential profile - decays with conductance tau_d_AMPA
	A_NMDA       : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_r_NMDA
        B_NMDA       : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_d_NMDA
}

INITIAL{

        LOCAL tp_AMPA, tp_NMDA
        
	A_AMPA = 0
        B_AMPA = 0
	
	A_NMDA = 0
	B_NMDA = 0
        
	tp_AMPA = (tau_r_AMPA*tau_d_AMPA)/(tau_d_AMPA-tau_r_AMPA)*log(tau_d_AMPA/tau_r_AMPA) :time to peak of the conductance
	tp_NMDA = (tau_r_NMDA*tau_d_NMDA)/(tau_d_NMDA-tau_r_NMDA)*log(tau_d_NMDA/tau_r_NMDA) :time to peak of the conductance
        
	factor_AMPA = -exp(-tp_AMPA/tau_r_AMPA)+exp(-tp_AMPA/tau_d_AMPA) :AMPA Normalization factor - so that when t = tp_AMPA, gsyn = gpeak
        factor_AMPA = 1/factor_AMPA
	
	factor_NMDA = -exp(-tp_NMDA/tau_r_NMDA)+exp(-tp_NMDA/tau_d_NMDA) :NMDA Normalization factor - so that when t = tp_NMDA, gsyn = gpeak
        factor_NMDA = 1/factor_NMDA
	eventCounter = 0

}

BREAKPOINT {

        SOLVE state METHOD cnexp
	mggate = 1 / (1 + exp(0.062 (/mV) * -(v)) * (mg / 3.57 (mM))) :mggate kinetics - Jahr & Stevens 1990
        g_AMPA = gmax*(B_AMPA-A_AMPA) :compute time varying conductance as the difference of state variables B_AMPA and A_AMPA
	g_NMDA = gmax*(B_NMDA-A_NMDA) * mggate :compute time varying conductance as the difference of state variables B_NMDA and A_NMDA and mggate kinetics
        i_AMPA = g_AMPA*(v-e) :compute the AMPA driving force based on the time varying conductance, membrane potential, and AMPA reversal
	i_NMDA = g_NMDA*(v-e) :compute the NMDA driving force based on the time varying conductance, membrane potential, and NMDA reversal
	i = i_AMPA + i_NMDA
}

DERIVATIVE state{

        A_AMPA' = -A_AMPA/tau_r_AMPA
        B_AMPA' = -B_AMPA/tau_d_AMPA
	A_NMDA' = -A_NMDA/tau_r_NMDA
        B_NMDA' = -B_NMDA/tau_d_NMDA
}


NET_RECEIVE (weight, Pv, Pr, u, myInd, tsyn (ms)){
	
	:printf("NMDA weight = %g\n", weight_NMDA)

        INITIAL{
                Pv=1
                u=u0
		eventCounter=0
            }

        :Randomize which of the synapses is activated. Note that an additional random number is generated by rand() - this may interfere with the random number order in parallel simulations.
        VERBATIM
          void** vv = (void**)(&space);
          void** vv2 = (void**)(&space2);
          double *x;
          int nx = vector_instance_px(*vv, &x);
          double *x2;
          int nx2 = vector_instance_px(*vv2, &x2);
          int myInd = 0;
          if (eventCounter < nx2) {
            myInd = x2[(int)eventCounter];
            //printf("eventCounter < nx2. t = %lf, eventCounter = %lf, nx2 = %i, myInd = %i\n",t, eventCounter, nx2, myInd);
          }
          else printf("eventCounter >= nx2! t = %lf, eventCounter = %lf, nx2 = %i\n",t, eventCounter, nx2);
	  eventCounter++;
          _args[4] = myInd;
          _args[5] = x[myInd];                //tsyn
          _args[1] = x[myInd+(int)Nsyns];     //Pv
          _args[3] = x[myInd+2*((int)Nsyns)]; //u
        ENDVERBATIM
	::printf("NET_RECEIVE_beg: Pv = %g, Pr = %g, u = %g, myInd = %g, tsyn = %g, t = %g\n", Pv, Pr, u, myInd, tsyn, t)
	:printf("NET_RECEIVE_beg:  myInd = %g/%g, Pv = %g, u = %g, tsyn = %g, t = %g. ", myInd, Nsyns, Pv, u, tsyn, t)

        : calc u at event-
        if (Fac > 0) {
              u = u*exp(-(t - tsyn)/Fac) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
        } else {
              u = Use  
        } 
        if(Fac > 0){
              u = u + Use*(1-u) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
        }    

        
        Pv  = 1 - (1-Pv) * exp(-(t-tsyn)/Dep) :Probability Pv for a vesicle to be available for release, analogous to the pool of synaptic
                                              :resources available for release in the deterministic model. Eq. 3 in Fuhrmann et al.
        Pr  = u * Pv                          :Pr is calculated as Pv * u (running value of Use)
        Pv  = Pv - u * Pv                     :update Pv as per Eq. 3 in Fuhrmann et al.
        :printf("Pv = %g\n", Pv)
        :printf("Pr = %g\n", Pr)
        tsyn = t

	if (erand() < Pr){
            A_AMPA = A_AMPA + weight*factor_AMPA
            B_AMPA = B_AMPA + weight*factor_AMPA
            A_NMDA = A_NMDA + weight*weight_factor_NMDA*factor_NMDA
            B_NMDA = B_NMDA + weight*weight_factor_NMDA*factor_NMDA
	    :::printf("Exc Released! Pr = %g, Pv = %g, u = %g, myInd = %g, tsyn = %g\n", Pr, Pv, u, myInd, tsyn)
	    ::printf("Exc Released!\n")
            :printf ( "R! Pr = %g\n" , Pr )
          } else {
	    :::printf("Exc Not released! Pr = %g, Pv = %g, u = %g, myInd = %g, tsyn = %g\n", Pr, Pv, u, myInd, tsyn)
            ::printf("Exc Not released! value = %g, Pr = %g\n", erand(), Pr )
            :printf ( "NR! Pr = %g\n" , Pr )
          }
	:printf("NET_RECEIVE_end: Pv = %g, Pr = %g, u = %g, myInd = %g, tsyn = %g, t = %g\n", Pv, Pr, u, myInd, tsyn, t)

        VERBATIM
          x[myInd] = t;
          x[myInd+(int)Nsyns] = _args[1];
	  x[myInd+2*((int)Nsyns)] = _args[3];
        ENDVERBATIM
}

PROCEDURE setRNG() {
VERBATIM
    {
        /**
         * This function takes a NEURON Random object declared in hoc and makes it usable by this mod file.
         * Note that this method is taken from Brett paper as used by netstim.hoc and netstim.mod
         * which points out that the Random must be in negexp(1) mode
         */
        void** pv = (void**)(&_p_rng);
        if( ifarg(1)) {
            *pv = nrn_random_arg(1);
        } else {
            *pv = (void*)0;
        }
    }
ENDVERBATIM
}

FUNCTION erand() {
VERBATIM
	    //FILE *fi;
        double value;
        if (_p_rng) {
                /*
                :Supports separate independent but reproducible streams for
                : each instance. However, the corresponding hoc Random
                : distribution MUST be set to Random.negexp(1)
                */
                value = nrn_random_pick(_p_rng);
		        //fi = fopen("RandomStreamMCellRan4.txt", "w");
                //fprintf(fi,"random stream for this simulation = %lf\n",value);
                //printf("random stream for this simulation = %lf\n",value);
                return value;
        }else{
ENDVERBATIM
                : the old standby. Cannot use if reproducible parallel sim
                : independent of nhost or which host this instance is on
                : is desired, since each instance on this cpu draws from
                : the same stream
                erand = exprand(1)
VERBATIM
        }
ENDVERBATIM
        :erand = value :This line must have been a mistake in Hay et al.'s code, it would basically set the return value to a non-initialized double value.
                       :The reason it sometimes works could be that the memory allocated for the non-initialized happened to contain the random value
                       :previously generated. However, here we commented this line out.
}

PROCEDURE setVec() {    : Sets the times of firing of each synapse. This should be done only once for each ProbAMPANMDA2group,
                        : before the running of the simulation, and the underlying vector should be untouched after that.
  VERBATIM
  void** vv;
  vv = (void**)(&space);
  *vv = (void*)0;
  if (ifarg(1)) {
    *vv = vector_arg(1);
    Nsyns = vector_capacity(*vv)/3;
  }
  ENDVERBATIM
}
PROCEDURE setVec2() {    : Sets the IDs of the synapses to fire
  VERBATIM
  void** vv;
  vv = (void**)(&space2);
  *vv = (void*)0;
  if (ifarg(1)) {
    *vv = vector_arg(1);
    Nevents = vector_capacity(*vv);
  }
  ENDVERBATIM
}

PROCEDURE printVec() { : Prints the previous times of firing of each synapse.
VERBATIM
    void** vv = (void**)(&space);
    double *x;
    int nx = vector_instance_px(*vv, &x);
    int i1;
    for (i1=0;i1<Nsyns;i1++) {
      printf("tsyns[%i] = %g, Pv[%i] = %g, u[%i] = %g\n", i1, x[i1], i1, x[i1+(nx/3)], i1, x[i1+2*(nx/3)]);
    }
ENDVERBATIM
}
PROCEDURE printVec2() { : Prints the previous times of firing of each synapse.
VERBATIM
    void** vv = (void**)(&space2);
    double *x;
    int nx = vector_instance_px(*vv, &x);
    int i1;
    for (i1=0;i1<Nevents;i1++) {
      printf("%g ", x[i1]);
      if (i1%100==99)
        printf("\n");
    }
ENDVERBATIM
}


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