Thalamocortical augmenting response (Bazhenov et al 1998)

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Accession:37819
In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
Reference:
1 . Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998) Computational models of thalamocortical augmenting responses. J Neurosci 18:6444-65 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Thalamus;
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,t; I T low threshold; I A; I K,Ca;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synchronization; Synaptic Integration;
Implementer(s): Lytton, William [bill.lytton at downstate.edu];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex L5/6 pyramidal GLU cell; GabaA; GabaB; AMPA; I Na,t; I T low threshold; I A; I K,Ca; Gaba; Glutamate;
: $Id: nstim.mod,v 1.5 2004/03/15 22:43:00 billl Exp $

NEURON	{ 
  ARTIFICIAL_CELL NStim
  RANGE y
  RANGE interval, number, start, end
  RANGE noise,fflag
}

PARAMETER {
	interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
	number	= 10 <0,1e9>	: number of spikes
	start		= 50 (ms)	: start of first spike
	noise		= 0 <0,1>	: amount of randomeaness (0.0 - 1.0)
	end		= 1e9 (ms)	: time to terminate train
        fflag           = 1             : don't change
}

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

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 0
	y = 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)
	}
}	

PROCEDURE init_sequence(t(ms)) {
	if (number > 0) {
		on = 1
		event = t
		endt = 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 > endt || 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
	}
}

INCLUDE "pointer.inc"