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]
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;
function [TrialNeurons, z, active] = ParseZscores (StimCount, neurons, tstop, SpikeTimes, meanFiringRate, meanSTD)
StimSpace = tstop/ StimCount;

    TrialNeurons= zeros(StimCount, neurons);
    for i = 1:size(SpikeTimes, 1)
        trialNo = ceil(SpikeTimes(i, 2) / StimSpace);
        neuronNo = SpikeTimes(i, 1);
        if (neuronNo < neurons)
            TrialNeurons(trialNo, neuronNo+1) =         TrialNeurons(trialNo, neuronNo+1) + 1;


% as opposed to mean and std, mean2 std2 calculate for all matrix values
mTN = mean2(TrialNeurons);
stdTN = std2(TrialNeurons);
% Because comparing neurons to all neurons in the exp does not work
% I had to use the baseline firing rate of 0.5Hz and 
mTN = meanFiringRate ; %0.5;
stdTN = meanSTD ;% 0.2;
z = ( TrialNeurons - mTN ) / stdTN;

for i = 1:size(z, 1)
    for j = 1:size(z,2)
        if z(i,j) > 2.58
        active(i,j) = 1;
            active(i,j) = 0;


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