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
experiment
data
ampa.mod
ampain.mod
cadyn.mod
cadynin.mod
cal.mod
calc.mod
calcb.mod
can.mod
cancr.mod
canin.mod
car.mod
cat.mod
catcb.mod
gabaa.mod *
gabaain.mod
gabab.mod
h.mod
hcb.mod
hin.mod
ican.mod
iccb.mod
iccr.mod
icin.mod
iks.mod
ikscb.mod
ikscr.mod
iksin.mod
kadist.mod
kadistcr.mod
kadistin.mod
kaprox.mod
kaproxcb.mod
kaproxin.mod
kca.mod
kcain.mod
kct.mod
kctin.mod
kdr.mod *
kdrcb.mod
kdrcr.mod
kdrin.mod
naf.mod
nafcb.mod
nafcr.mod
nafin.mod
nafx.mod
nap.mod *
netstim.mod *
NMDA.mod
NMDAIN.mod
sinclamp.mod *
cb.hoc
cr.hoc
ExperimentControl.hoc *
final.hoc
incell.hoc
net.hoc
pfc_pc_temp.hoc
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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
}


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