Closed AGuyNextDoor closed 3 years ago
wvangeit
commented
3 years ago I tried to compile the MOD files myself on Big Sur 11.2, but I don't get an error. Which version of neuron are you using? Could you maybe try to install neuron using 'pip install neuron' ?
In case you modified the MOD file, could you provide the modified version.
Thank you.
AGuyNextDoor
commented
3 years ago Thank you for your answer.
I'm using python3.8 and NEURON 7.8.2 I've re-downloaded the git file multiple times so it should be the current version which really puts me off on finding the bug
Maybe python3 is at fault even if I'm not really sure how this would be because it depends more on the nrnivmodl function
I'm managed to "temporarily" solve it. I saw that a line was missing in the mod file for the derivative of R so I added the R' from the TsodyksMarkram_AMPA_NMDA.mod file
: Declaration of ODEs solved for in the BREAKPOINT block
DERIVATIVE odes {
A_AMPA' = -A_AMPA/tau_r_AMPA
B_AMPA' = -B_AMPA/tau_d_AMPA
A_NMDA' = -A_NMDA/tau_r_NMDA
B_NMDA' = -B_NMDA/tau_d_NMDA
R' = (1-R)/tau_rec
Use' = -Use/tau_facil
}After that it seemed to compile fine but I'm not really sure if I can trust my model...
My current mod version of the StochasticTsodyksMarkram_AMPA_NMDA
COMMENT
/**
* @file TsodyksMarkram_AMPA_NMDA.mod
* @brief An AMPA and NMDA glutamate receptor model with Tsodyks-Markram dynamics of the releasible pool
* @author emuller
* @date 2017-05-11
* @remark Copyright © BBP/EPFL 2005-2014; All rights reserved.
*/
ENDCOMMENT
TITLE AMPA and NMDA glutamate receptor with Stochastic Tsodyks-Markram dynamics
COMMENT
AMPA and NMDA glutamate receptor conductance using a dual-exponential profile
and with stochastic Tsodyks-Markram dynamics of the releasible pool.
This new model is based on Fuhrmann et al. 2002, and has the following properties:
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.
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 Use (which follows facilitation dynamics). If it
releases, it will transition to the unrecovered state. Recovery is as
a Poisson process with rate 1/tau_rec.
ENDCOMMENT
: Definition of variables which may be accesses by the user
NEURON {
THREADSAFE
POINT_PROCESS StochasticTsodyksMarkram_AMPA_NMDA
RANGE tau_r_AMPA, tau_d_AMPA
RANGE tau_r_NMDA, tau_d_NMDA
RANGE mg, gmax_AMPA, gmax_NMDA
RANGE i, i_AMPA, i_NMDA, g_AMPA, g_NMDA, g, e
RANGE tau_rec, tau_facil, U1
NONSPECIFIC_CURRENT i
: For user defined random number generator
POINTER rng
}
: Definition of constants which may be set by the user
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
e = 0 (mV) : AMPA and NMDA reversal potential
mg = 1 (mM) : Initial concentration of mg2+
mggate
gmax_AMPA = .001 (uS) : Weight conversion factor (from nS to uS)
gmax_NMDA = .001 (uS) : Weight conversion factor (from nS to uS)
: Parameters for Tsodyks-Markram (TM) model of vesicle pool dynamics
tau_rec = 600 (ms) : time constant of vesicle pool recovery
tau_facil = 10 (ms) : time constant of release facilitation relaxation
U1 = 0.5 (1) : release probability in the absence of facilitation
}
: Declaration of state variables
STATE {
A_AMPA : AMPA state variable to construct the dual-exponential profile
: rise kinetics with tau_r_AMPA
B_AMPA : AMPA state variable to construct the dual-exponential profile
: decay kinetics with tau_d_AMPA
A_NMDA : NMDA state variable to construct the dual-exponential profile
: rise kinetics with tau_r_NMDA
B_NMDA : NMDA state variable to construct the dual-exponential profile
: decay kinetics with tau_d_NMDA
: State variables for TM model
R : running fraction of vesicle pool
Use : running release probability
}
: This is "inline" code in the C programming language
: needed to manage user defined random number generators
VERBATIM
#include<stdlib.h>
#include<stdio.h>
#include<math.h>
double nrn_random_pick(void* r);
void* nrn_random_arg(int argpos);
ENDVERBATIM
: Declaration of variables that are computed, e.g. in the BREAKPOINT block
ASSIGNED {
v (mV)
i (nA)
i_AMPA (nA)
i_NMDA (nA)
g_AMPA (uS)
g_NMDA (uS)
g (uS)
factor_AMPA
factor_NMDA
rng
}
: Definition of initial conditions
INITIAL{
LOCAL tp_AMPA, tp_NMDA : Declaration of some local variables
: Zero receptor rise and fall kinetics variables
A_AMPA = 0
B_AMPA = 0
A_NMDA = 0
B_NMDA = 0
: Compute constants needed to normalize the dual-exponential receptor dynamics
: Expression for time to peak of the AMPA dual-exponential conductance
tp_AMPA = (tau_r_AMPA*tau_d_AMPA)/(tau_d_AMPA-tau_r_AMPA)*log(tau_d_AMPA/tau_r_AMPA)
: Expression for time to peak of the NMDA dual-exponential conductance
tp_NMDA = (tau_r_NMDA*tau_d_NMDA)/(tau_d_NMDA-tau_r_NMDA)*log(tau_d_NMDA/tau_r_NMDA)
: AMPA Normalization factor - so that when t = tp_AMPA, gsyn = gpeak
factor_AMPA = -exp(-tp_AMPA/tau_r_AMPA)+exp(-tp_AMPA/tau_d_AMPA)
factor_AMPA = 1/factor_AMPA
: NMDA Normalization factor - so that when t = tp_NMDA, gsyn = gpeak
factor_NMDA = -exp(-tp_NMDA/tau_r_NMDA)+exp(-tp_NMDA/tau_d_NMDA)
factor_NMDA = 1/factor_NMDA
R = 1
Use = 0
}
: Declare method to propagate the state variables in time
BREAKPOINT {
: Specify to solve system of equations "odes", declared below (DERIVATIVE block)
: "cnexp" specifies the intergration method, it is
: an implicit integration method that is stable even for stiff systems
SOLVE odes METHOD cnexp
: Compute and assign quantities which depend on the state variables
: Compute the time varying AMPA receptor conductance as
: the difference of state variables B_AMPA and A_AMPA
g_AMPA = gmax_AMPA*(B_AMPA-A_AMPA)
: NMDA is similar, but with a Magnesium block term: mggate
: Magneisum block kinetics due to Jahr & Stevens 1990
mggate = 1 / (1 + exp(0.062 (/mV) * -(v)) * (mg / 3.57 (mM)))
g_NMDA = mggate*gmax_NMDA*(B_NMDA-A_NMDA)
: Total conductance
g = g_AMPA + g_NMDA
: Compute the AMPA and NMDA specific currents
i_AMPA = g_AMPA*(v-e)
i_NMDA = g_NMDA*(v-e)
: Compute the total current
i = i_AMPA + i_NMDA
}
: Declaration of ODEs solved for in the BREAKPOINT block
DERIVATIVE odes {
A_AMPA' = -A_AMPA/tau_r_AMPA
B_AMPA' = -B_AMPA/tau_d_AMPA
A_NMDA' = -A_NMDA/tau_r_NMDA
B_NMDA' = -B_NMDA/tau_d_NMDA
R' = (1-R)/tau_rec
Use' = -Use/tau_facil
}
: Block to be executed for a pre-synaptic spike event
NET_RECEIVE (weight, tsyn (ms)) {
LOCAL A, result, Psurv
INITIAL{
tsyn=t
}
Use = Use + U1*(1-Use) : Update of release probability
if (R == 0) {
: probability of survival of unrecovered state based on Poisson recovery with rate 1/tau_rec
Psurv = exp(-(t-tsyn)/tau_rec)
result = urand()
if (result>Psurv) {
: recover
R = 1
}
else {
: probability of survival must now be from this interval
tsyn = t
}
}
if (R == 1) {
result = urand()
if (result<Use) {
: release!
tsyn = t
R = 0
A_AMPA = A_AMPA + weight*factor_AMPA
B_AMPA = B_AMPA + weight*factor_AMPA
A_NMDA = A_NMDA + weight*factor_NMDA
B_NMDA = B_NMDA + weight*factor_NMDA
}
}
}
: Black magic to support use defined random number generators
: No user serviceable code below this point
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.uniform(0,1)
*/
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
}
I'm not sure your change will indeed be the correct one. I think it's better to remove R from the state variables, and put it into the ASSIGNED block
AGuyNextDoor
commented
3 years ago This seems to work fine!
Can't record _ref_R anymore but doesn't seem to be used for this exercise!
Thanks a lot!
wvangeit
commented
3 years ago to record it, it also need to be in the RANGE list above
Hello,
I'm having trouble compiling the mod files from the week 4 exercises.
When executing the following command
I get the following error
There seems to be an error with the StochasticTsodyksMarkram_AMPA_NMDA.mod file. When I delete the file, the compiling goes fine but it makes me unable to do the graded exercise for this week.
This is run locally on MacOS Big Sur 11.2, using jupyter notebook with a trusted kernel.
Anyone has an idea? I tried to look in the mod file but being really unfamiliar with the format I can't seem to find the possible syntax error or bug.