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;
objref LayerVector, strobj
strdef cmdstr, layers, tmpstr

proc SetOtherParameters() {local ind, i, layernum, tempheight
	strobj = new StringFunctions()
	tmpstr = LayerHeights
	ind = strobj.substr(tmpstr,";")
	layers = tmpstr
	strobj.left(layers, ind)
	sscanf(layers,"%g", &layernum)
	{sprint(cmdstr, "LayerVector=new Vector(%g)",  layernum)} // List of Zo (starting height) values, 1 for each layer
	for i=0, layernum-1 {
		strobj.right(tmpstr, ind+1)
		ind = strobj.substr(tmpstr,";")
		layers = tmpstr
		strobj.left(layers, ind)
		sscanf(layers,"%g", &tempheight)
		LayerVector.x[i] = tempheight

	axconvel = 250	// axonal conduction velocity in microns/ms (Bartos et al. 2002)

	tstart = 0		// Start time of simulation
	tstop = SimDuration	// End time of simulation
	dt = TemporalResolution		// Integration interval for fadvance (see NEURON reference)
	secondorder = 2	// A global variable which specifies the time integration method
					// 	(see NEURON reference)
	v_init = -60	// All voltages of all sections are initialized to this voltage (mV)
	N = 1			// Specifies step resolution (see NEURON reference for steps_per_ms),
					// 	sometimes 1, sometimes 4

	steps_per_ms = 1*N/(dt)		// Controls the intervals at which points are plotted
	noise_random_stream_offset_ = (tstop - tstart)/dt + 1	// Each noise generator picks a number every
															// 	dt for the full time range, so stream
															// 	sections must be at least this long to
															// 	prevent overlap
	OK2executeSysCmds = 1 	// If you don't like the NEURON code to make new directories, concatenate files,
							//  or delete files, set this parameter to 0. Watch out though, if this is set to
							//  0, the code will use whatever RunName you supply or 'none' if you do not
							//  supply a RunName. This means:
							//  - the program will expect a directory named 'RunName' to be in the results directory
							//  - the program will overwrite any existing files in the ./results/RunName directory
	if (OK2executeSysCmds==0) {
		CatFlag = 0			// Overwrite the CatFlag if necessary. If it is specified that executing system commands
							//  is *not* okay, we definitely do not want CatFlag to be 1 (do not allow concatenating
							//  results files)