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]
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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PFCcell
mechanism
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TITLE simple AMPA receptors

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

	Simple model for glutamate AMPA receptors
	=========================================

  - FIRST-ORDER KINETICS, FIT TO WHOLE-CELL RECORDINGS

    Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate
    receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used
    to estimate the parameters of the present model; the fit was performed
    using a simplex algorithm (see Destexhe et al., J. Computational Neurosci.
    1: 195-230, 1994).

  - SHORT PULSES OF TRANSMITTER (0.3 ms, 0.5 mM)

    The simplified model was obtained from a detailed synaptic model that 
    included the release of transmitter in adjacent terminals, its lateral 
    diffusion and uptake, and its binding on postsynaptic receptors (Destexhe
    and Sejnowski, 1995).  Short pulses of transmitter with first-order
    kinetics were found to be the best fast alternative to represent the more
    detailed models.

  - ANALYTIC EXPRESSION

    The first-order model can be solved analytically, leading to a very fast
    mechanism for simulating synapses, since no differential equation must be
    solved (see references below).



References

   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. Synthesis of models for
   excitable membranes, synaptic transmission and neuromodulation using a 
   common kinetic formalism, Journal of Computational Neuroscience 1: 
   195-230, 1994.

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



NEURON {
	POINT_PROCESS GLU         
	RANGE R, gmax, g, ina     
	USEION na WRITE ina   
	NONSPECIFIC_CURRENT  iglu             : i
	GLOBAL Cdur, Alpha, Beta, Erev, Rinf, Rtau
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER {
        Cmax	= 1	(mM)		: max transmitter concentration
:	Cdur	= 0.3	(ms)		: transmitter duration (rising phase)
	Cdur	= 1.1	(ms)		: transmitter duration (rising phase)
:	Alpha	= 0.94	(/ms)	: forward (binding) rate
	Alpha	= 10	(/ms)	: forward (binding) rate
:	Beta	= 0.18	(/ms)		: backward (unbinding) rate
	Beta	= 0.5	(/ms)		: backward (unbinding) rate
	Erev	= 0	(mV)		:0 reversal potential
}


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

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)
	iglu = g*(v - Erev)  :i
	ina = 0.9*iglu
	iglu = 0.1*iglu

}

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
}


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