Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)

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Accession:187604
This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1
References:
1 . Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I (2016) Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. Elife [PubMed]
2 . Bezaire M, Raikov I, Burk K, Armstrong C, Soltesz I (2016) SimTracker tool and code template to design, manage and analyze neural network model simulations in parallel NEURON bioRxiv
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal cell; Hippocampus CA1 interneuron oriens alveus cell; Hippocampus CA1 basket cell; Hippocampus CA1 stratum radiatum interneuron; Hippocampus CA1 bistratified cell; Hippocampus CA1 axo-axonic cell; Hippocampus CA1 PV+ fast-firing interneuron;
Channel(s): I Na,t; I K; I K,leak; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s): GabaA; GabaB; Glutamate; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; NEURON (web link to model);
Model Concept(s): Oscillations; Methods; Connectivity matrix; Laminar Connectivity; Gamma oscillations;
Implementer(s): Bezaire, Marianne [mariannejcase at gmail.com]; Raikov, Ivan [ivan.g.raikov at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; Hippocampus CA1 interneuron oriens alveus cell; GabaA; GabaB; Glutamate; Gaba; I Na,t; I K; I K,leak; I h; I K,Ca; I Calcium; Gaba; Glutamate;
: $Id: netstim.mod 2212 2008-09-08 14:32:26Z hines $
: comments at end

NEURON	{ 
  ARTIFICIAL_CELL MyNetStim
  RANGE interval, number, start
  RANGE noise
  RANGE sid, cid
  RANGE xpos, ypos, zpos, gid, randi
  THREADSAFE : only true if every instance has its own distinct Random
  POINTER donotuse
}

PARAMETER {
	interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
	number	= 10 <0,1e9>	: number of spikes (independent of noise)
	start		= 50 (ms)	: start of first spike
	noise		= 0 <0,1>	: amount of randomness (0.0 - 1.0)
	sid = -1 (1) : synapse id, from cell template
	cid = -1 (1) : id of cell to which this synapse is attached
	xpos = 0
	ypos = 0
	zpos = 0
	gid = 0
	randi = 0
}

ASSIGNED {
	event (ms)
	on
	ispike
	donotuse
}

PROCEDURE seed(a) {
	set_seed(a)
}

INITIAL {
	on = 0 : off
	ispike = 0
	if (noise < 0) {
		noise = 0
	}
	if (noise > 1) {
		noise = 1
	}
	if (start >= 0 && number > 0) {
		on = 1
		: 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 = 0
		ispike = 0
	}
}

FUNCTION is_art() {
	is_art=1
}

PROCEDURE position(a, b, c) { 
	xpos = a
	ypos = b
	zpos = c
}

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*erand()
	}
}
VERBATIM
double nrn_random_pick(void* r);
void* nrn_random_arg(int argpos);
ENDVERBATIM

FUNCTION erand() {
VERBATIM
	if (_p_donotuse) {
		/*
		:Supports separate independent but reproducible streams for
		: each instance. However, the corresponding hoc Random
		: distribution MUST be set to Random.negexp(1)
		*/
		_lerand = nrn_random_pick(_p_donotuse);
	}else{
		/* only can be used in main thread */
		if (_nt != nrn_threads) {
hoc_execerror("multithread random in NetStim"," only via hoc Random");
		}
ENDVERBATIM
		: the old standby. Cannot use if reproducible parallel sim
		: independent of nhost or which host this instance is on
		: is desired, since each instance on this cpu draws from
		: the same stream
		erand = exprand(1)
VERBATIM
	}
ENDVERBATIM
}

PROCEDURE noiseFromRandom() {
VERBATIM
 {
	void** pv = (void**)(&_p_donotuse);
	if (ifarg(1)) {
		*pv = nrn_random_arg(1);
	}else{
		*pv = (void*)0;
	}
 }
ENDVERBATIM
}

PROCEDURE next_invl() {
	if (number > 0) {
		event = invl(interval)
	}
	if (ispike >= number) {
		on = 0
	}
}

NET_RECEIVE (w) {
	if (flag == 0) { : external event
		if (w > 0 && on == 0) { : turn on spike sequence
			: but not if a netsend is on the queue
			init_sequence(t)
			: randomize the first spike so on average it occurs at
			: noise*interval (most likely interval is always 0)
			next_invl()
			event = event - interval*(1. - noise)
			net_send(event, 1)
		}else if (w < 0) { : turn off spiking definitively
			on = 0
		}
	}
	if (flag == 3) { : from INITIAL
		if (on == 1) { : but ignore if turned off by external event
			init_sequence(t)
			net_send(0, 1)
		}
	}
	if (flag == 1 && on == 1) {
		ispike = ispike + 1
		net_event(t)
		next_invl()
		if (on == 1) {
			net_send(event, 1)
		}
	}
}

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: 	number of spikes (independent of noise)

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 (no net_send events on queue)
 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 an on!=0 state,
the stimulator receives a negative weight event, the stimulator will
change to the on==0 state. In the on==0 state, it will ignore any ariving
net_send events. A change to the on==1 state immediately fires the first spike of
its sequence.

ENDCOMMENT


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