COMMENT
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ENDCOMMENT

TITLE Probabilistic AMPA and NMDA receptor with presynaptic short-term plasticity 


COMMENT
AMPA and NMDA receptor conductance using a dual-exponential profile
presynaptic short-term plasticity as in Fuhrmann et al. 2002

_EMS (Eilif Michael Srikanth)
Modification of ProbAMPANMDA: 2-State model by Eilif Muller, Michael Reimann, Srikanth Ramaswamy, Blue Brain Project, August 2011
This new model was motivated by the following constraints:

1) No consumption on failure.  
2) No release just after release until recovery.
3) Same ensemble averaged trace as deterministic/canonical Tsodyks-Markram 
   using same parameters determined from experiment.
4) Same quantal size as present production probabilistic model.

To satisfy these constaints, the synapse is implemented as a
uni-vesicular (generalization to multi-vesicular should be
straight-forward) 2-state Markov process.  The states are
{1=recovered, 0=unrecovered}.

For a pre-synaptic spike or external spontaneous release trigger
event, the synapse will only release if it is in the recovered state,
and with probability u (which follows facilitation dynamics).  If it
releases, it will transition to the unrecovered state.  Recovery is as
a Poisson process with rate 1/Dep.

This model satisfies all of (1)-(4).
ENDCOMMENT

COMMENT                                                                          
/**                                                                              
 @file ProbAMPANMDA_EMS.mod                                                        
 @brief Probabilistic AMPA and NMDA receptor with presynaptic short-term plasticity                   
 @author Eilif Muller, Michael Reimann, Srikanth Ramaswamy, James King @ BBP     
 @date 2011                                                                      
*/                                                                               
ENDCOMMENT  

NEURON {
    THREADSAFE
        POINT_PROCESS ProbAMPANMDA_EMS_verbose
        RANGE tau_r_AMPA, tau_d_AMPA, tau_r_NMDA, tau_d_NMDA
        RANGE Use, u, Dep, Fac, u0, mg, Rstate, tsyn_fac, u
        RANGE i, i_AMPA, i_NMDA, g_AMPA, g_NMDA, g, e, NMDA_ratio, gmax
        RANGE A_AMPA_step, B_AMPA_step, A_NMDA_step, B_NMDA_step
        NONSPECIFIC_CURRENT i
        POINTER rng
        RANGE synapseID, verboseLevel
}

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 release probability
        synapseID = 0
        verboseLevel = 0
	NMDA_ratio = 0.71 (1) : The ratio of NMDA to AMPA
}

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)
        i_AMPA (nA)
        i_NMDA (nA)
        g_AMPA (uS)
        g_NMDA (uS)
        g (uS)
        factor_AMPA
        factor_NMDA
        A_AMPA_step
        B_AMPA_step
        A_NMDA_step
        B_NMDA_step
        rng

	: Recording these three, you can observe full state of model
	: tsyn_fac gives you presynaptic times, Rstate gives you 
        : state transitions,
        : u gives you the "release probability" at transitions 
        : (attention: u is event based based, so only valid at incoming events)
	Rstate (1) : recovered state {0=unrecovered, 1=recovered}
	tsyn_fac (ms) : the time of the last spike
	u (1) : running release probability

}

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

	Rstate=1
	tsyn_fac=0
	u=u0
        
        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

        A_AMPA_step = exp(dt*(( - 1.0 ) / tau_r_AMPA))
        B_AMPA_step = exp(dt*(( - 1.0 ) / tau_d_AMPA))
        A_NMDA_step = exp(dt*(( - 1.0 ) / tau_r_NMDA))
        B_NMDA_step = exp(dt*(( - 1.0 ) / tau_d_NMDA))
}

BREAKPOINT {

        SOLVE state
        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
        g = g_AMPA + g_NMDA
        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
}

PROCEDURE state() {
        A_AMPA = A_AMPA*A_AMPA_step
        B_AMPA = B_AMPA*B_AMPA_step
        A_NMDA = A_NMDA*A_NMDA_step
        B_NMDA = B_NMDA*B_NMDA_step
}


NET_RECEIVE (weight,weight_AMPA, weight_NMDA, Psurv, tsyn (ms)){
        LOCAL result
        weight_AMPA = weight
        weight_NMDA = weight * NMDA_ratio
	: Locals:
	: Psurv - survival probability of unrecovered state
	: tsyn - time since last surival evaluation.
	
        INITIAL{
                tsyn=t
        }

    : Do not perform any calculations if the synapse (netcon) is deactivated.  This avoids drawing from the random stream
    if(  !(weight > 0) ) {
VERBATIM
        return;
ENDVERBATIM
    }

        : calc u at event-
        if (Fac > 0) {
                u = u*exp(-(t - tsyn_fac)/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.
           }    

	   : tsyn_fac knows about all spikes, not only those that released
	   : i.e. each spike can increase the u, regardless of recovered state.
	   tsyn_fac = t

	   : recovery

	   if (Rstate == 0) {
	   : probability of survival of unrecovered state based on Poisson recovery with rate 1/tau
	          Psurv = exp(-(t-tsyn)/Dep)
		  result = urand()
		  if (result>Psurv) {
		         Rstate = 1     : recover      

                         printf( "Recovered! %f at time %g: Psurv = %g, urand=%g\n", synapseID, t, Psurv, result )

		  }
		  else {
		         : survival must now be from this interval
		         tsyn = t
                         printf( "Failed to recover! %f at time %g: Psurv = %g, urand=%g\n", synapseID, t, Psurv, result )
		  }
           }	   
	   
	   if (Rstate == 1) {
   	          result = urand()
		  if (result<u) {
		  : release!
   		         tsyn = t
			 Rstate = 0
                         A_AMPA = A_AMPA + weight_AMPA*factor_AMPA
                         B_AMPA = B_AMPA + weight_AMPA*factor_AMPA
                         A_NMDA = A_NMDA + weight_NMDA*factor_NMDA
                         B_NMDA = B_NMDA + weight_NMDA*factor_NMDA
                         
                             printf( "Release! %f at time %g: vals %g %g %g %g\n", synapseID, t, A_AMPA, weight_AMPA, factor_AMPA, weight )
		  		  
		  }
		  else {
			     printf("Failure! %f at time %g: urand = %g\n", synapseID, t, result )

		  }

	   }

}

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 uniform(1) mode
         */
        void** pv = (void**)(&_p_rng);
        if( ifarg(1)) {
            *pv = nrn_random_arg(1);
        } else {
            *pv = (void*)0;
        }
    }
ENDVERBATIM
}

FUNCTION urand() {
VERBATIM
        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);
                //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
                value = scop_random(1)
VERBATIM
        }
ENDVERBATIM
        urand = value
}

FUNCTION toggleVerbose() {
    verboseLevel = 1-verboseLevel
}