Biophysically realistic neuron models for simulation of cortical stimulation (Aberra et al. 2018)

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Accession:241165
This archive instantiates the single-cell cortical models used in (Aberra et al. 2018) and sets up extracellular stimulation with either a point-current source, to simulate intracortical microstimulation (ICMS), or a uniform E-field distribution, with a monophasic, rectangular pulse waveform in both cases.
Reference:
1 . Aberra AS, Peterchev AV, Grill WM (2018) Biophysically realistic neuron models for simulation of cortical stimulation J. Neural Eng. [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon;
Brain Region(s)/Organism: Neocortex; Barrel cortex;
Cell Type(s): Myelinated neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Detailed Neuronal Models;
Implementer(s): Aberra, Aman [aman.aberra at duke.edu];
/
AberraEtAl2018
cells
L23_PC_cADpyr229_5
mechanisms
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
ProbAMPANMDA_EMS.mod *
ProbGABAAB_EMS.mod *
SK_E2.mod *
SKv3_1.mod *
                            
COMMENT
/*                                                                               
Copyright (c) 2015 EPFL-BBP, All rights reserved.                                
                                                                                 
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WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE             
OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN           
IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.                                    
                                                                                 
This work is licensed under a                                                    
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 
To view a copy of this license, visit                                            
http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode or send a letter to   
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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
        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
        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      

                         if( verboseLevel > 0 ) {
                             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
                         if( verboseLevel > 0 ) {
                             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
                         
                         if( verboseLevel > 0 ) {
                             printf( "Release! %f at time %g: vals %g %g %g %g\n", synapseID, t, A_AMPA, weight_AMPA, factor_AMPA, weight )
                         }
		  		  
		  }
		  else {
		         if( verboseLevel > 0 ) {
			     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
}

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