## Direction of current in point process

NMODL and the Channel Builder.
cafischer
Posts: 19
Joined: Sun Nov 30, 2014 6:03 am

### Direction of current in point process

I am using a model for an Ornstein-Uhlenbeck Process (see below). It is defined as a point process. What does this imply regarding the direction of current? Is i > 0 depolarizing the cell, so that the equation for a one-compartment model would be c_m dV/dt = -I_ion + I_syn or is it - I_syn?

Code: Select all

``````INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
POINT_PROCESS Gfluct
RANGE g_e, g_i, E_e, E_i, g_e0, g_i0, g_e1, g_i1
RANGE std_e, std_i, tau_e, tau_i, D_e, D_i
RANGE new_seed
NONSPECIFIC_CURRENT i
}

UNITS {
(nA) = (nanoamp)
(mV) = (millivolt)
(umho) = (micromho)
}

PARAMETER {
dt		(ms)

E_e	= 0 	(mV)	: reversal potential of excitatory conductance
E_i	= -75 	(mV)	: reversal potential of inhibitory conductance

g_e0	= 0.0121 (umho)	: average excitatory conductance
g_i0	= 0.0573 (umho)	: average inhibitory conductance

std_e	= 0.0030 (umho)	: standard dev of excitatory conductance
std_i	= 0.0066 (umho)	: standard dev of inhibitory conductance

tau_e	= 2.728	(ms)	: time constant of excitatory conductance
tau_i	= 10.49	(ms)	: time constant of inhibitory conductance
}

ASSIGNED {
v	(mV)		: membrane voltage
i 	(nA)		: fluctuating current
g_e	(umho)		: total excitatory conductance
g_i	(umho)		: total inhibitory conductance
g_e1	(umho)		: fluctuating excitatory conductance
g_i1	(umho)		: fluctuating inhibitory conductance
D_e	(umho umho /ms) : excitatory diffusion coefficient
D_i	(umho umho /ms) : inhibitory diffusion coefficient
exp_e
exp_i
amp_e	(umho)
amp_i	(umho)
}

INITIAL {
g_e1 = 0
g_i1 = 0
if(tau_e != 0) {
D_e = 2 * std_e * std_e / tau_e
exp_e = exp(-dt/tau_e)
amp_e = std_e * sqrt( (1-exp(-2*dt/tau_e)) )
}
if(tau_i != 0) {
D_i = 2 * std_i * std_i / tau_i
exp_i = exp(-dt/tau_i)
amp_i = std_i * sqrt( (1-exp(-2*dt/tau_i)) )
}
}

BREAKPOINT {
SOLVE oup
if(tau_e==0) {
g_e = std_e * normrand(0,1)
}
if(tau_i==0) {
g_i = std_i * normrand(0,1)
}
g_e = g_e0 + g_e1
g_i = g_i0 + g_i1
i = g_e * (v - E_e) + g_i * (v - E_i)
}

PROCEDURE oup() {		: use Scop function normrand(mean, std_dev)
if(tau_e!=0) {
g_e1 =  exp_e * g_e1 + amp_e * normrand(0,1)
}
if(tau_i!=0) {
g_i1 =  exp_i * g_i1 + amp_i * normrand(0,1)
}
}
``````

ted
Posts: 5770
Joined: Wed May 18, 2005 4:50 pm
Location: Yale University School of Medicine
Contact:

### Re: Direction of current in point process

NEURON uses the standard sign convention that neurophysiologists do. A NONSPECIFIC_CURRENT is just a transmembrane ionic current that is not attributed to a particular ionic species. It follows the same, time-honored sign convention that every other transmembrane current does--depolarizing current is negative, and hyperpolarizing current is positive. Current injected into a cell by a microelectrode is an ELECTRODE_CURRENT, and has the corresponding sign convention: depolarizing current is positive and hyperpolarizing current is negative.

Where did you get that OUP code? It's guaranteed to produce wildly incorrect results if either tau_e or tau_i (or both) equal zero. I ask so we can track it down and hopefully prevent others from getting the same buggy code.

Here is a revised version that (1) does not suffer from that bug, and (2) contains the original notes by Alain Destexhe, plus an explanation of the bug and how it was fixed.

Code: Select all

``````TITLE Fluctuating conductances

COMMENT
-----------------------------------------------------------------------------

Fluctuating conductance model for synaptic bombardment
======================================================

THEORY

Synaptic bombardment is represented by a stochastic model containing
two fluctuating conductances g_e(t) and g_i(t) descibed by:

Isyn = g_e(t) * [V - E_e] + g_i(t) * [V - E_i]
d g_e / dt = -(g_e - g_e0) / tau_e + sqrt(D_e) * Ft
d g_i / dt = -(g_i - g_i0) / tau_i + sqrt(D_i) * Ft

where E_e, E_i are the reversal potentials, g_e0, g_i0 are the average
conductances, tau_e, tau_i are time constants, D_e, D_i are noise diffusion
coefficients and Ft is a gaussian white noise of unit standard deviation.

g_e and g_i are described by an Ornstein-Uhlenbeck (OU) stochastic process
where tau_e and tau_i represent the "correlation" (if tau_e and tau_i are
zero, g_e and g_i are white noise).  The estimation of OU parameters can
be made from the power spectrum:

S(w) =  2 * D * tau^2 / (1 + w^2 * tau^2)

and the diffusion coeffient D is estimated from the variance:

D = 2 * sigma^2 / tau

NUMERICAL RESOLUTION

The numerical scheme for integration of OU processes takes advantage
of the fact that these processes are gaussian, which led to an exact
update rule independent of the time step dt (see Gillespie DT, Am J Phys
64: 225, 1996):

x(t+dt) = x(t) * exp(-dt/tau) + A * N(0,1)

where A = sqrt( D*tau/2 * (1-exp(-2*dt/tau)) ) and N(0,1) is a normal
random number (avg=0, sigma=1)

IMPLEMENTATION

This mechanism is implemented as a nonspecific current defined as a
point process.

PARAMETERS

The mechanism takes the following parameters:

E_e = 0  (mV)    : reversal potential of excitatory conductance
E_i = -75 (mV)    : reversal potential of inhibitory conductance

g_e0 = 0.0121 (umho)  : average excitatory conductance
g_i0 = 0.0573 (umho)  : average inhibitory conductance

std_e = 0.0030 (umho)  : standard dev of excitatory conductance
std_i = 0.0066 (umho)  : standard dev of inhibitory conductance

tau_e = 2.728 (ms)    : time constant of excitatory conductance
tau_i = 10.49 (ms)    : time constant of inhibitory conductance

REFERENCE

Destexhe, A., Rudolph, M., Fellous, J-M. and Sejnowski, T.J.
Fluctuating synaptic conductances recreate in-vivo--like activity in
neocortical neurons. Neuroscience 107: 13-24 (2001).

(electronic copy available at http://cns.iaf.cnrs-gif.fr)

A. Destexhe, 1999

-----------------------------------------------------------------------------

20150414 -- Ted Carnevale
Fixed so that zero value for tau_e or tau_i results in
"white noise" fluctuation of g_e or g_i.
In the previous implementation, tau_e or tau_i == 0
made g_e or g_i equal to g_e0 or g_i0.

The fix involved
1.  restructuring conditional statements so that
zero value for tau_e or tau_i had desired effect,
and
2.  moving all calls to normrand from the BREAKPOINT block
into PROCEDURE oup.

The latter was necessary because code in the BREAKPOINT block
is executed twice per time step in order to estimate di/dv
(the slope conductance of this "channel") for the Jacobian matrix.
The estimate is simply
di/dv ~ (i(v+0.001) - i(v))/0.001
where the current i is calculated by a statement of the form
i = f(v)
and f() is an algebraic expression that involves v.

Clearly if the algebraic expression involves terms that change
from one call to the next, the estimate of di/dv will be incorrect,
and the solution will be corrupted.
The most obvious symptom of this is an occasional, abrupt, large
jump of v, but even step-to-step small fluctuations of v will be
incorrect.

The solution is to relegate calls to random number generators
either to a PROCEDURE that is SOLVEd, or to a BEFORE BREAKPOINT block,
because code in those blocks is executed only once per advance.
ENDCOMMENT

NEURON {
POINT_PROCESS Gfluct
RANGE g_e, g_i, E_e, E_i, g_e0, g_i0, g_e1, g_i1
RANGE std_e, std_i, tau_e, tau_i, D_e, D_i
RANGE new_seed
NONSPECIFIC_CURRENT i
}

UNITS {
(nA) = (nanoamp)
(mV) = (millivolt)
(umho) = (micromho)
}

PARAMETER {
dt    (ms)

E_e  = 0   (mV)  : reversal potential of excitatory conductance
E_i  = -75   (mV)  : reversal potential of inhibitory conductance

g_e0  = 0.0121 (umho)  : average excitatory conductance
g_i0  = 0.0573 (umho)  : average inhibitory conductance

std_e  = 0.0030 (umho)  : standard dev of excitatory conductance
std_i  = 0.0066 (umho)  : standard dev of inhibitory conductance

tau_e  = 2.728  (ms)  : time constant of excitatory conductance
tau_i  = 10.49  (ms)  : time constant of inhibitory conductance
}

ASSIGNED {
v  (mV)    : membrane voltage
i   (nA)    : fluctuating current
g_e  (umho)    : total excitatory conductance
g_i  (umho)    : total inhibitory conductance
g_e1  (umho)    : fluctuating excitatory conductance
g_i1  (umho)    : fluctuating inhibitory conductance
D_e  (umho umho /ms) : excitatory diffusion coefficient
D_i  (umho umho /ms) : inhibitory diffusion coefficient
exp_e
exp_i
amp_e  (umho)
amp_i  (umho)
}

INITIAL {
g_e1 = 0
g_i1 = 0
if(tau_e != 0) {
D_e = 2 * std_e * std_e / tau_e
exp_e = exp(-dt/tau_e)
amp_e = std_e * sqrt( (1-exp(-2*dt/tau_e)) )
}
if(tau_i != 0) {
D_i = 2 * std_i * std_i / tau_i
exp_i = exp(-dt/tau_i)
amp_i = std_i * sqrt( (1-exp(-2*dt/tau_i)) )
}
}

BREAKPOINT {
SOLVE oup
i = g_e * (v - E_e) + g_i * (v - E_i)
}

PROCEDURE oup() {    : use Scop function normrand(mean, std_dev)
if(tau_e==0) {
g_e = g_e0 + std_e * normrand(0,1)
} else {
g_e1 = exp_e * g_e1 + amp_e * normrand(0,1)
g_e = g_e0 + g_e1
}
if (g_e < 0) { g_e = 0 }
if(tau_i==0) {
g_i = g_i0 + std_i * normrand(0,1)
} else {
g_i1 = exp_i * g_i1 + amp_i * normrand(0,1)
g_i = g_i0 + g_i1
}
if (g_i < 0) { g_i = 0 }
}

PROCEDURE new_seed(seed) {    : procedure to set the seed
set_seed(seed)
VERBATIM
printf("Setting random generator with seed = %g\n", _lseed);
ENDVERBATIM
}
``````

cafischer
Posts: 19
Joined: Sun Nov 30, 2014 6:03 am

### Re: Direction of current in point process

Thanks a lot!
I found this mechanism 3 years ago, so I don't remember where I found it (I guess on modeldb). Could well be that it is already fixed now.