Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells (Lee et al. 2014)

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This detailed microcircuit model explores the network level effects of sublayer specific connectivity in the mouse CA1. The differences in strengths and numbers of synapses between PV+ basket cells and either superficial sublayer or deep sublayer pyramidal cells enables a routing of inhibition from superficial to deep pyramidal cells. At the network level of this model, the effects become quite prominent when one compares the effect on firing rates when either the deep or superficial pyramidal cells receive a selective increase in excitation.
1 . Lee SH, Marchionni I, Bezaire M, Varga C, Danielson N, Lovett-Barron M, Losonczy A, Soltesz I (2014) Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells. Neuron 82:1129-44 [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: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell;
Channel(s): I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): GabaA; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Connectivity matrix; Laminar Connectivity;
Implementer(s): Bezaire, Marianne [mariannejcase at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; Glutamate; I Sodium; I Calcium; I Potassium;
{load_file("nrngui.hoc")}				// Standard definitions - NEURON library file

{load_file("setupfiles/defaultvar.hoc")}	// Contains the proc definition for default_var proc
{default_var("NumData",100)}		// for paired recording, use these synapse weights
strdef cmdstr
objref f2
	f2 = new File()
	sprint(cmdstr, "datasets/cellnumbers_%g.dat", NumData)
	f2.ropen(cmdstr)		// Open the celltype
	numCellTypes = f2.scanvar       // Scan the first line, which contains a number giving the
									// # cell types, including stimulating (artificial) cells (still?)

objref celltypestring[numCellTypes], techstring[numCellTypes], cellType[numCellTypes], cell
double cellnumvar[numCellTypes], cellLayerflag[numCellTypes], cellArtflag[numCellTypes]

begintemplate CellCategoryInfo
	public cellType_string, technicalType, setCellTypeParams
	strdef cellType_string, technicalType
	proc setCellTypeParams(){
		cellType_string = $s1		// Name of the cell type
		technicalType = $s2
endtemplate CellCategoryInfo	

for i=0, numCellTypes-1 {
	celltypestring[i]= new String()
	techstring[i] = new String()
	f2.scanstr(celltypestring[i].s)				// Scan in the cell name
	cellnumvar[i]=f2.scanvar					// Scan in the initial (before sclerosis) number of each cell type
	cellLayerflag[i]=f2.scanvar					// Scan the layer flag (hilar=2, granular=1, molecular=0), where hilar cells
	cellArtflag[i]=f2.scanvar					// Scan the layer flag (hilar=2, granular=1, molecular=0), where hilar cells

	cellType[i] = new CellCategoryInfo(i)	// Make one object for each cell type to store cell type info
	cellType[i].setCellTypeParams(celltypestring[i].s, techstring[i].s)


strdef cmdstr, tempFileStr
for i = 0, numCellTypes-1 {
	if (cellArtflag[i]==0) {
		sprint(tempFileStr,"./cells/class_%s.hoc", cellType[i].technicalType)	// Concatenate the
		load_file(tempFileStr)			// Load the file with the template that defines the class
		sprint(cmdstr, "cell = new %s(i,i)", cellType[i].technicalType)