Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)

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Accession:168314
This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network.
References:
1 . Hummos A, Franklin CC, Nair SS (2014) Intrinsic mechanisms stabilize encoding and retrieval circuits differentially in a hippocampal network model. Hippocampus 24:1430-48 [PubMed]
2 . Hummos A, Nair SS (2017) An integrative model of the intrinsic hippocampal theta rhythm. PLoS One 12:e0182648 [PubMed]
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): Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron; Abstract Izhikevich neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Acetylcholine; Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Epilepsy; Storage/recall;
Implementer(s):
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Acetylcholine; Gaba; Glutamate;
StimFreq = 60 //(Hz)
StimCount = 1
StimSpace = tstop / StimCount
StimDurRatio = 0.8

pool = new Vector(Totals.x(EC))
pool.indgen(0, 29, 1)

	objref Pat1, Pat2
	Pat1 = new Vector()
	Pat1 = GenerateVector(12)

	
for j = 0, StimCount-1 {
	stim= new NetStim(0.5)
	stim.interval = (1/ StimFreq) * 1000
	stim.start = j * (StimSpace) +1
	stim.number = 1 //(StimSpace * StimDurRatio) / stim.interval

	// Pat2 = new Vector()
	// Pat2 = GenerateVector(1)
	// Pat1.append(Pat2.x(0))
	
	applyStim(stim, Pat1, 10)
	Stims.append(stim)
} //for j

obfunc ParseTrials() { localobj spikes, activity, spikeT, spikeID
	spikeT = $o1
	spikeID = $o2
	simTime = $3
	binCount = $4
	neuronCount = $5

	binDuration = simTime/binCount


	activity = new Matrix(neuronCount, binCount)

	for t = 0, spikeT.size() -1 {
		ind = int(spikeT.x(t) / binDuration)
		activity.x(spikeID.x(t), ind) +=1
	} // for t

	return (activity)
}




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