Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)

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Accession:231427
This work (published in "Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001).
Reference:
1 . Doron M, Chindemi G, Muller E, Markram H, Segev I (2017) Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons. Cell Rep 21:1550-1561 [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 V1 L6 pyramidal corticothalamic 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): NMDA; GabaA; AMPA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models;
Implementer(s): Doron, Michael [michael.doron at mail.huji.ac.il];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; GabaA; AMPA; NMDA; 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; Gaba; Glutamate;
/
reproduction
readme.txt
ampa.mod
Ca_HVA.mod
Ca_LVAst.mod *
cad.mod *
cadiffus.mod
CaDynamics_E2.mod *
canmda.mod *
car.mod *
gabaa.mod *
gabab.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
nmda.mod *
ProbAMPA.mod
ProbAMPANMDA2_ratio.mod
ProbUDFsyn2_lark.mod
SK_E2.mod *
SKv3_1.mod *
SynExp5NMDA.mod *
cell1.asc *
cellmorphology.hoc *
create_data_for_figure_01.py
create_data_for_figure_02.py
create_data_for_figure_03.py *
create_data_for_figure_03_control.py
create_data_for_figure_03_Dt_10.py *
create_data_for_figure_03_Dt_40.py *
data_same_excitation.pickle
iniparameter.hoc
L5PCbiophys3.hoc
L5PCbiophys3_noActive.hoc
mosinit.hoc
plot_figure_01.py
plot_figure_02.py
plot_figure_03.py
plot_figure_04.py
plot_figure_05.py
plot_figure_06.py
spikes_num.pickle
spine.hoc
TTC.hoc
                            
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

ENDCOMMENT


NEURON {

        POINT_PROCESS ProbUDFsyn2_lark
        RANGE tau_r, tau_d
        RANGE Use, u, Dep, Fac, u0
        RANGE i, g, e, gmax
        NONSPECIFIC_CURRENT i
	POINTER rng
}

PARAMETER {

        tau_r = 0.2   (ms)  : dual-exponential conductance profile
        tau_d = 1.7    (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
    	gmax = .001 (uS) : weight conversion factor (from nS to uS)
    	u0 = 0 :initial value of u, which is the running value of Use
}

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);

ENDVERBATIM
  

ASSIGNED {

        v (mV)
        i (nA)
	g (uS)
        factor
	rng
	weight_NMDA
}

STATE {
        A       : state variable to construct the dual-exponential profile - decays with conductance tau_r_AMPA
        B       : state variable to construct the dual-exponential profile - decays with conductance tau_d_AMPA
}

INITIAL{

  LOCAL tp
        
	A = 0
  B = 0
	
        
	tp = (tau_r*tau_d)/(tau_d-tau_r)*log(tau_d/tau_r) :time to peak of the conductance
	      
	factor = -exp(-tp/tau_r)+exp(-tp/tau_d) : Normalization factor - so that when t = tp, gsyn = gpeak
        factor = 1/factor
 
}

BREAKPOINT {

        SOLVE state METHOD cnexp
        g = gmax*(B-A) :compute time varying conductance as the difference of state variables B and A
        i = g*(v-e) :compute the driving force based on the time varying conductance, membrane potential, and reversal
}

DERIVATIVE state{

        A' = -A/tau_r
        B' = -B/tau_d
}


NET_RECEIVE (weight, Pv, Pr, u, tsyn (ms)){
	
        INITIAL{
                Pv=1
                u=u0
                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
        
            A = A + weight*factor
            B = B + weight*factor
                
}

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
}

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