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

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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
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 GLU cell; Hippocampus CA1 interneuron oriens alveus GABA 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;
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 GLU cell; Hippocampus CA1 interneuron oriens alveus GABA cell; GabaA; GabaB; Glutamate; Gaba; I Na,t; I K; I K,leak; I h; I K,Ca; I Calcium; Gaba; Glutamate;
'ca1' model code repository
Written by Marianne Bezaire, marianne.bezaire@gmail.com, www.mariannebezaire.com
In the lab of Ivan Soltesz, www.ivansolteszlab.org
Published and latest versions of this code are available online at:
Open Source Brain: http://www.opensourcebrain.org/projects/nc_ca1

Main code file: ../main.hoc
This file: Last updated on April 10, 2015

This file defines two procedures related to spontaneous stimulation,
but is different than the spontaneous_stimulation file in that it
only provides stimulation locally to mypoint:
1. connectStims: this is used when each cell needs to have its own,
   unique excitatory spike train input. This procedure connects
   a specific artificial cell to each real cell of the model, so that
   each real cell has its own independent input. When this procedure
   is used, then the artificial cells should NOT be connected to the
   real cells within the connectivity algorithm. If the connectivity
   algorithm is used instead, the difference is that any particular
   artificial cell could be connected to 0, 1, or multiple real cells
   in lines with whatever positioning and axonal distribution properties
   have been specified. This is a good alternative for when the
   artificial cells are meant to mimic real inputs to a network.
2. setNoise: this procedure sets the properties of the random number
   generators that are then associated to the noise functionality of
   the artificial cells. Each artificial cell has its own, unique
   random stream that is associated to its noise algorithm. The noise
   algorithm then draws from that random number stream to determine
   the interspike intervals that define the spike train pattern of the cell.
   However, only artificial cells whose positions are within a certain
   region or within a certain distance of a point will be stimulated;
   the other cells will be silenced throughout the simulation.

// The connectStims procedure connects a specific artificial cell to each real cell of the model, and it
//  does this for each artificial cell type. It expects there to be enough artificial cells of each type
//  that a unique artificial cell can be connected with each real cell of the model. The specific
//  artificial cell that is connected to each real cell has a gid that is related to the gid of the real
//  cell.
proc connectStims() { local wgt, reli, typei, jgid, postcelltype, precelltype localobj cell
	for precelltype=0, numCellTypes-1 {	// For each potential presynaptic cell type
		if (cellType[precelltype].is_art==1) {	// If the cell is an artificial cell type
			for postcelltype=0, numCellTypes-1 {	// For each potential postsynaptic cell type
				if (cellType[postcelltype].is_art==0) {	// If the cell is a real cell type
					wgt = cellType[precelltype].wgtConns.x[postcelltype]	// Check the weight specified for this connection
					if (wgt > 0) {											//  and if stronger than 0, make the connections
						// For each cell of type postsynaptic cell type owned by this processor
						for pcitr(&reli, &typei, &jgid, cellType[postcelltype].cellStartGid, cellType[postcelltype].cellEndGid) {
							if (pc.gid_exists(jgid)) {
								cell = pc.gid2cell(jgid)	// Create a reference to the postsynaptic cell

								if (cell.pre_list.o(precelltype).count()>0) {	// If potential synapses have been defined for this connection type
									// Make a single synapse connection from a specific artificial cell to a specific real cell, 
									//  where the cells that are connected are related by their gids.
									nc_appendo(jgid-cellType[postcelltype].cellEndGid-1+cellType[precelltype].cellStartGid, cell, precelltype, 0, wgt, 3)
									cellType[precelltype].numCons.x[postcelltype] +=1	// Increment the connection counter

if (MakeArtConns==0) {	// Only make connections from artificial cells if they 
	connectStims()		//  have not been connected at another time (ie, in the
}						//  connectivity algorithm)

proc setNoise() {local reli, typei, jgid, idx localobj cell 
	// This procedure takes two arguments:
	//  $1 = x position of the point in microns
	//  $2 = y position of the point in microns
	for idx=0, numCellTypes-1 {
		if (cellType[idx].is_art==1) {	// If the cell type is artificial, set the random stream
										//  associated with the noise in its spike times
			// For all the artificial cells of this type, owned by this processor
			for pcitr(&reli, &typei, &jgid, cellType[idx].cellStartGid, cellType[idx].cellEndGid) {
				cell = pc.gid2cell(jgid)	// Create a reference to the artificial cell
				if (sqrt((cell.xpos-$1)*(cell.xpos-$1) + (cell.ypos-$2)*(cell.ypos-$2))<MaxEDist) { // relative to a point
				//if ((cell.x<1000) && (cell.y<700) && (cell.y>200)) { // or by absolute position
					ranstimlist.object(reli).r.negexp(1) 	// Using a negative exponential distribution to determine
															//  the interspike interval (ISI) gives Poisson-distributed 
															//  spike times. Always use 1 as the argument; the interval
															//  for the netstim will set the 'rate' (or average ISI).
															// For another example (which defines the randomstream class 
															//  differently, and therefore calls different commands at 
															//  different times) see the following file:
															// <http://senselab.med.yale.edu/modeldb/showmodel.asp?model=83319&file=\destexhe_benchmarks\NEURON\common\netstim.hoc>
					cell.noiseFromRandom(ranstimlist.object(reli).r)	// Set this random stream as the random number
																		//  generator for the noise functionality of
																		//  the spike generator.
				} else {
					cell.start = -1 // This cell will be silent the whole simulation (unless it receives positive input from another cell)


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