Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)

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Accession:144089
"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. ... Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression. "
Reference:
1 . Sidiropoulou K, Poirazi P (2012) Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons. PLoS Comput Biol 8:e1002489 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I A; I K; I K,Ca; I CAN;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA; IP3;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Detailed Neuronal Models;
Implementer(s): Sidiropoulou, Kyriaki [sidirop at imbb.forth.gr];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; IP3; I Na,p; I Na,t; I L high threshold; I A; I K; I K,Ca; I CAN; Gaba; Glutamate;
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TITLE minimal model of GABAa receptors

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

	Minimal kinetic model for GABA-A receptors
	==========================================

  Model of Destexhe, Mainen & Sejnowski, 1994:

	(closed) + T <-> (open)

  The simplest kinetics are considered for the binding of transmitter (T)
  to open postsynaptic receptors.   The corresponding equations are in
  similar form as the Hodgkin-Huxley model:

	dr/dt = alpha * [T] * (1-r) - beta * r

	I = gmax * [open] * (V-Erev)

  where [T] is the transmitter concentration and r is the fraction of 
  receptors in the open form.

  If the time course of transmitter occurs as a pulse of fixed duration,
  then this first-order model can be solved analytically, leading to a very
  fast mechanism for simulating synaptic currents, since no differential
  equation must be solved (see Destexhe, Mainen & Sejnowski, 1994).

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

  Based on voltage-clamp recordings of GABAA receptor-mediated currents in rat
  hippocampal slices (Otis and Mody, Neuroscience 49: 13-32, 1992), this model
  was fit directly to experimental recordings in order to obtain the optimal
  values for the parameters (see Destexhe, Mainen and Sejnowski, 1996).

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

  This mod file includes a mechanism to describe the time course of transmitter
  on the receptors.  The time course is approximated here as a brief pulse
  triggered when the presynaptic compartment produces an action potential.
  The pointer "pre" represents the voltage of the presynaptic compartment and
  must be connected to the appropriate variable in oc.

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

  See details in:

  Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  An efficient method for
  computing synaptic conductances based on a kinetic model of receptor binding
  Neural Computation 6: 10-14, 1994.  

  Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  Kinetic models of 
  synaptic transmission.  In: Methods in Neuronal Modeling (2nd edition; 
  edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1996.


  Written by Alain Destexhe, Laval University, 1995

-----------------------------------------------------------------------------
ENDCOMMENT

NEURON {
	POINT_PROCESS GABAa
	RANGE R, g, gmax 
	NONSPECIFIC_CURRENT i
	GLOBAL Cmax, Cdur, Alpha, Beta, Erev, Rinf, Rtau
	RANGE i
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER {

	Cmax	= 1	(mM)		: max transmitter concentration
	Cdur	= 1	(ms)		: transmitter duration (rising phase)
	Alpha	= 5	(/ms mM)	: forward (binding) rate
	Beta	= 0.18	(/ms)		: backward (unbinding) rate
	Erev	= -80	(mV)		: reversal potential
}


ASSIGNED {
	v		(mV)		: postsynaptic voltage
	i 		(nA)		: current = g*(v - Erev)
	g 		(umho)		: conductance
	Rinf				: steady state channels open
	Rtau		(ms)		: time constant of channel binding
        synon
       	gmax
}

STATE {Ron Roff}



INITIAL {

	Rinf = Cmax*Alpha / (Cmax*Alpha + Beta)
	Rtau = 1 / ((Alpha * Cmax) + Beta)
	synon = 0
}

BREAKPOINT {
	SOLVE release METHOD cnexp
	g = (Ron + Roff)*1(umho)
	i = g*(v - Erev)
}

DERIVATIVE release {
	Ron' = (synon*Rinf - Ron)/Rtau
	Roff' = -Beta*Roff
}

: following supports both saturation from single input and
: summation from multiple inputs
: if spike occurs during CDur then new off time is t + CDur
: ie. transmitter concatenates but does not summate
: Note: automatic initialization of all reference args to 0 except first

NET_RECEIVE(weight, on, nspike, r0, t0 (ms)) {
	: flag is an implicit argument of NET_RECEIVE and  normally 0
        if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
		nspike = nspike + 1
		if (!on) {
			r0 = r0*exp(-Beta*(t - t0))
			t0 = t
			on = 1
			synon = synon + weight
			state_discontinuity(Ron, Ron + r0)
			state_discontinuity(Roff, Roff - r0)
		}
		: come again in Cdur with flag = current value of nspike
		net_send(Cdur, nspike)
        }
	if (flag == nspike) { : if this associated with last spike then turn off
		r0 = weight*Rinf + (r0 - weight*Rinf)*exp(-(t - t0)/Rtau)
		t0 = t
		synon = synon - weight
		state_discontinuity(Ron, Ron - r0)
		state_discontinuity(Roff, Roff + r0)
		on = 0
	}
gmax=weight
}