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

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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.
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]
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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): 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;
Gap Junctions:
Transmitter(s): Acetylcholine; Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Epilepsy; Storage/recall;
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Acetylcholine; Gaba; Glutamate;
objref synLi, conLi, vecLi, stimLi
synLi = new List()
conLi = new List()
vecLi = new List()
stimLi = new List()

obfunc GenPoisson() { localobj train
train = new Vector()
time = 0
lambda = $1
T = $2

	while (time < T) {
		isi = - log( rand.uniform(0,1) ) / lambda
		time = time + isi

proc ApplyRandomInput () {localobj synli, conli, vecli, stimli, cellsli
// cellsli = new List()
cellsli = $o1
lambda = $2
T =  $3
weight = $4
for kk = 0, cellsli.count()-1 {
	stimli = new VecStim()
	vecli = GenPoisson(lambda, T)
	// vecli = removeZeros(vecli)
	cellsli.o(kk).soma synli = new pyr2pyr(0.9)
	conli = new NetCon(stimli, synli, 0,0,1)
		synli.Wmax = weight
		synli.Wmin = 0
		synli.initW = weight

} // ApplyRandom

// Background NOISE
ApplyRandomInput(Cells.o(1), 0.5/1000, tstop, 5) // CA3 
ApplyRandomInput(Cells.o(4), 0.5 /1000, tstop, 5)// DG