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_4
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                                                    
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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 GABAAB receptor with presynaptic short-term plasticity 


COMMENT
GABAA receptor conductance using a dual-exponential profile
presynaptic short-term plasticity based on Fuhrmann et al, 2002
Implemented by Srikanth Ramaswamy, Blue Brain Project, March 2009

_EMS (Eilif Michael Srikanth)
Modification of ProbGABAA: 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 ProbGABAAB_EMS.mod
 @brief GABAAB receptor with presynaptic short-term plasticity
 @author Eilif Muller, Michael Reimann, Srikanth Ramaswamy, James King @ BBP
 @date 2011
*/
ENDCOMMENT

NEURON {
    THREADSAFE
	POINT_PROCESS ProbGABAAB_EMS
	RANGE tau_r_GABAA, tau_d_GABAA, tau_r_GABAB, tau_d_GABAB 
	RANGE Use, u, Dep, Fac, u0, Rstate, tsyn_fac, u
	RANGE i,i_GABAA, i_GABAB, g_GABAA, g_GABAB, g, e_GABAA, e_GABAB, GABAB_ratio
        RANGE A_GABAA_step, B_GABAA_step, A_GABAB_step, B_GABAB_step
	NONSPECIFIC_CURRENT i
    POINTER rng
    RANGE synapseID, verboseLevel
}

PARAMETER {
	tau_r_GABAA  = 0.2   (ms)  : dual-exponential conductance profile
	tau_d_GABAA = 8   (ms)  : IMPORTANT: tau_r < tau_d
    tau_r_GABAB  = 3.5   (ms)  : dual-exponential conductance profile :Placeholder value from hippocampal recordings SR
	tau_d_GABAB = 260.9   (ms)  : IMPORTANT: tau_r < tau_d  :Placeholder value from hippocampal recordings 
	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_GABAA    = -80     (mV)  : GABAA reversal potential
    e_GABAB    = -97     (mV)  : GABAB reversal potential
    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
	GABAB_ratio = 0 (1) : The ratio of GABAB to GABAA
}

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_GABAA (nA)
        i_GABAB (nA)
        g_GABAA (uS)
        g_GABAB (uS)
        A_GABAA_step
        B_GABAA_step
        A_GABAB_step
        B_GABAB_step
	g (uS)
	factor_GABAA
        factor_GABAB
        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_GABAA       : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAA
        B_GABAA       : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAA
        A_GABAB       : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAB
        B_GABAB       : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAB
}

INITIAL{

        LOCAL tp_GABAA, tp_GABAB

	Rstate=1
	tsyn_fac=0
	u=u0
        
        A_GABAA = 0
        B_GABAA = 0
        
        A_GABAB = 0
        B_GABAB = 0
        
        tp_GABAA = (tau_r_GABAA*tau_d_GABAA)/(tau_d_GABAA-tau_r_GABAA)*log(tau_d_GABAA/tau_r_GABAA) :time to peak of the conductance
        tp_GABAB = (tau_r_GABAB*tau_d_GABAB)/(tau_d_GABAB-tau_r_GABAB)*log(tau_d_GABAB/tau_r_GABAB) :time to peak of the conductance
        
        factor_GABAA = -exp(-tp_GABAA/tau_r_GABAA)+exp(-tp_GABAA/tau_d_GABAA) :GABAA Normalization factor - so that when t = tp_GABAA, gsyn = gpeak
        factor_GABAA = 1/factor_GABAA
        
        factor_GABAB = -exp(-tp_GABAB/tau_r_GABAB)+exp(-tp_GABAB/tau_d_GABAB) :GABAB Normalization factor - so that when t = tp_GABAB, gsyn = gpeak
        factor_GABAB = 1/factor_GABAB
        
        A_GABAA_step = exp(dt*(( - 1.0 ) / tau_r_GABAA))
        B_GABAA_step = exp(dt*(( - 1.0 ) / tau_d_GABAA))
        A_GABAB_step = exp(dt*(( - 1.0 ) / tau_r_GABAB))
        B_GABAB_step = exp(dt*(( - 1.0 ) / tau_d_GABAB))
}

BREAKPOINT {
	SOLVE state
	
        g_GABAA = gmax*(B_GABAA-A_GABAA) :compute time varying conductance as the difference of state variables B_GABAA and A_GABAA
        g_GABAB = gmax*(B_GABAB-A_GABAB) :compute time varying conductance as the difference of state variables B_GABAB and A_GABAB 
        g = g_GABAA + g_GABAB
        i_GABAA = g_GABAA*(v-e_GABAA) :compute the GABAA driving force based on the time varying conductance, membrane potential, and GABAA reversal
        i_GABAB = g_GABAB*(v-e_GABAB) :compute the GABAB driving force based on the time varying conductance, membrane potential, and GABAB reversal
        i = i_GABAA + i_GABAB
}

PROCEDURE state() {
        A_GABAA = A_GABAA*A_GABAA_step
        B_GABAA = B_GABAA*B_GABAA_step
        A_GABAB = A_GABAB*A_GABAB_step
        B_GABAB = B_GABAB*B_GABAB_step
}


NET_RECEIVE (weight, weight_GABAA, weight_GABAB, Psurv, tsyn (ms)){
    LOCAL result
    weight_GABAA = weight
    weight_GABAB = weight*GABAB_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_GABAA = A_GABAA + weight_GABAA*factor_GABAA
                         B_GABAA = B_GABAA + weight_GABAA*factor_GABAA
                         A_GABAB = A_GABAB + weight_GABAB*factor_GABAB
                         B_GABAB = B_GABAB + weight_GABAB*factor_GABAB
                         
                         if( verboseLevel > 0 ) {
                             printf( "Release! %f at time %g: vals %g %g %g \n", synapseID, t, A_GABAA, weight_GABAA, factor_GABAA )
                         }
		  		  
		  }
		  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
         */
        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.uniform(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
                urand = scop_random(1)
VERBATIM
        }
ENDVERBATIM
        urand = value
}

FUNCTION toggleVerbose() {
    verboseLevel = 1 - verboseLevel
}

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