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;
// Uses object NetStim to deliver one action potential to a synapse associated with EC neurons.  It divides the experiment time into 30 trials, and stimulates an increasing number of EC neurons each trial. 
StimFreq = 12 //(Hz)
StimCount = 30
StimSpace = tstop / StimCount
StimDurRatio = 0.5

load_file("StimuliNoise.hoc") // Delivers background noise to CA3 cells 
pool = new Vector(Totals.x(EC))
pool.indgen(0, 29, 1)

	objref Pat1, Pat2
	Pat1 = new Vector()
	// Pat1 = GenerateVector(1)

	
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

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