L5 PFC microcircuit used to study persistent activity (Papoutsi et al. 2014, 2013)

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Accession:155057
Using a heavily constrained biophysical model of a L5 PFC microcircuit we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes.
References:
1 . Papoutsi A, Sidiropoulou K, Cutsuridis V, Poirazi P (2013) Induction and modulation of persistent activity in a layer V PFC microcircuit model. Front Neural Circuits 7:161 [PubMed]
2 . Papoutsi A, Sidiropoulou K, Poirazi P (2014) Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model. PLoS Comput Biol 10:e1003764 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Connectionist Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I A; I CAN; I Potassium; I R; I_AHP;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Working memory;
Implementer(s): Papoutsi, Athanasia [athpapoutsi at gmail.com];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I A; I CAN; I Potassium; I R; I_AHP;
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L5microcircuit
mechanism
ampa.mod
ampain.mod
cadyn.mod
cal.mod
can.mod
car.mod
cat.mod
gabaa.mod *
gabaain.mod
gabab.mod
h.mod
ican.mod
iks.mod
kadist.mod
kca.mod
kct.mod
kdr.mod *
naf.mod
nap.mod
netstim.mod *
NMDA.mod
NMDA_syn.mod
sinclamp.mod *
vecstim.mod *
                            


NEURON	{ 
  ARTIFICIAL_CELL NetStim1
  RANGE y
  RANGE interval, number, start
  RANGE noise, burstP


}

PARAMETER {
	interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
	number 		= 10
        start		= 100 (ms)	: start of first spike
	noise		= 0 <0,1>	: amount of randomeaness (0.0 - 1.0)
	burstP		= 100 (ms)      : period of bursts or recursive events

}

ASSIGNED {
	y
	event (ms)
	on
	end (ms)
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 0
	y = 0
:	i = 0
	if (noise < 0) {
		noise = 0
	}
	if (noise > 1) {
		noise = 1
	}
	if (start >= 0 && number > 0) {
		: randomize the first spike so on average it occurs at
		: start + noise*interval
		event = start + invl(interval) - interval*(1. - noise)
		: but not earlier than 0
		if (event < 0) {
			event = 0
		}

		net_send(event, 3)
		net_send(event + burstP, 3)
		
		
	}
	
}
PROCEDURE init_sequence(t(ms)) { 
	if (number > 0) {
		on = 1
		event = t
		end = t + 1e-6 + invl(interval)*(number-1)
	}
}

FUNCTION invl(mean (ms)) (ms) {
	if (mean <= 0.) {
		mean = .01 (ms) : I would worry if it were 0.
	}
	if (noise == 0) {
		invl = mean
	}else{
		invl = (1. - noise)*mean + noise*mean*exprand(1)
	}
}

PROCEDURE event_time() {
	if (number > 0) {
		event = event + invl(interval)
	}
	if (event > end) {
		on = 0
	}
}

NET_RECEIVE (w) {
	if (flag == 0) { : external event
		if (w > 0 && on == 0) { : turn on spike sequence
			init_sequence(t)
			net_send(0, 1)
		}else if (w < 0 && on == 1) { : turn off spiking
			on = 0
		}
	}
	if (flag == 3) { : from INITIAL
		if (on == 0) {
			init_sequence(t)
			net_send(0, 1)
		}
	}
	if (flag == 1 && on == 1) {
		y = 2
		net_event(t)
		event_time()
		if (on == 1) {
			net_send(event - t, 1)
		}
		net_send(.1, 2)
	}
	if (flag == 2) {
		y = 0
	}
}

COMMENT
Presynaptic spike generator
---------------------------

This mechanism has been written to be able to use synapses in a single
neuron receiving various types of presynaptic trains.  This is a "fake"
presynaptic compartment containing a spike generator.  The trains
of spikes can be either periodic or noisy (Poisson-distributed)

Parameters;
   noise: 	between 0 (no noise-periodic) and 1 (fully noisy)
   interval: 	mean time between spikes (ms)
   number: 	mean number of spikes

Written by Z. Mainen, modified by A. Destexhe, The Salk Institute

Modified by Michael Hines for use with CVode
The intrinsic bursting parameters have been removed since
generators can stimulate other generators to create complicated bursting
patterns with independent statistics (see below)

Modified by Michael Hines to use logical event style with NET_RECEIVE
This stimulator can also be triggered by an input event.
If the stimulator is in the on=0 state and receives a positive weight
event, then the stimulator changes to the on=1 state and goes through
its entire spike sequence before changing to the on=0 state. During
that time it ignores any positive weight events. If, in the on=1 state,
the stimulator receives a negative weight event, the stimulator will
change to the off state. In the off state, it will ignore negative weight
events. A change to the on state immediately fires the first spike of
its sequence.

ENDCOMMENT


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