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
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
#include
#include
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__ 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
}
__