Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)

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Accession:168310
This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity
Reference:
1 . Konstantoudaki X, Papoutsi A, Chalkiadaki K, Poirazi P, Sidiropoulou K (2014) Modulatory effects of inhibition on persistent activity in a cortical microcircuit model. Front Neural Circuits 8:7 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron; Neocortex spiking irregular interneuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I h; I_Ks; I_KD;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Synchronization; Active Dendrites;
Implementer(s): Sidiropoulou, Kyriaki [sidirop at imbb.forth.gr]; Konstantoudaki, Xanthippi [xeniakons at gmail.com];
Search NeuronDB for information about:  GabaA; GabaB; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I h; I_Ks; I_KD; Gaba; Glutamate;
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KonstantoudakiEtAl2014
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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             
	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)
	Alpha	= 10	(/ms)		: forward (binding) rate
	Beta	= 0.11	(/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
}

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
}

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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