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

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Accession:153280
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.
Reference:
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;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Connectivity matrix; Laminar Connectivity;
Implementer(s): Bezaire, Marianne [mariannejcase at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; Glutamate; I Sodium; I Calcium; I Potassium; 
strdef cmdstr, cmdstr1, pre_type, post_type
objref f2c

proc conn_cells() {local num, wgt, syn
	for i=0, numCellTypes-1 {
		sprint(cmdstr, "%s_idx = %g", cellType[i].cellType_string, i)
		{execute1(cmdstr)}
		cellType[i].numConns = new Vector(numCellTypes)
		cellType[i].numSyns = new Vector(numCellTypes)
		cellType[i].wgtConns = new Vector(numCellTypes)
	}

	// load file
	f2c = new File()
	sprint(cmdstr, "../datasets/conndata_%g.dat", ConnData)
	f2c.ropen(cmdstr)		// Open the celltype
	connData = f2c.scanvar
	for r=0,connData-1 {
		f2c.scanstr(pre_type)
		f2c.scanstr(post_type)
		wgt = f2c.scanvar
		num = f2c.scanvar
		syn = f2c.scanvar

		sprint(cmdstr, "%s_idx", pre_type)
		sprint(cmdstr1, "%s_idx", post_type)
		if ((name_declared(cmdstr) > 0) && (name_declared(cmdstr1) > 0)) {
			sprint(cmdstr, "if ((strcmp(pre_type,\"granulecell\")==0) && (strcmp(post_type,\"granulecell\")==0)) {num = percentSprouting/100*cellType[%s_idx].numCells*cellType[%s_idx].numCells}", pre_type, post_type)
			{execute1(cmdstr)}

			sprint(cmdstr, "cellType[%s_idx].numConns.x[%s_idx] = %f", pre_type, post_type, num)
			{execute1(cmdstr)}
			
			sprint(cmdstr, "cellType[%s_idx].numSyns.x[%s_idx] = %f", pre_type, post_type, syn)
			{execute1(cmdstr)}
			
			sprint(cmdstr, "cellType[%s_idx].wgtConns.x[%s_idx] = %f", pre_type, post_type, wgt)
			{execute1(cmdstr)}
		}
	}
	f2c.close()
}
conn_cells()