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;
function [TrialNeurons, cor] = ParseTrials (StimCount, neurons, tstop, SpikeTimes, compareTo, norma)
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;
        end

    end

%     TrialNeurons = norma(TrialNeurons);
   if (norma >0 )
    for n = 1:size(TrialNeurons, 1)
%         if (norm(TrialNeurons(n, :)) > 0)
            TrialNeurons(n, :) =     TrialNeurons(n, :)./ norm(    TrialNeurons(n, :));
%         else
%             TrialNeurons(n, :) = 0.01;
%         end
    end
   end
    cor = [];

    for t = 1:size(TrialNeurons, 1)
        cor(end+1) = dot(TrialNeurons(compareTo, :), TrialNeurons(t, :) );
    end

end

function drawSpikeTimes (SpikeTimes)
    plot(SpikeTimes(:,2), SpikeTimes(:,1), 'r+', 'MarkerSize', 3);
end


function [TrialNeurons, cor] = ParseTrialsCS (StimCount, neurons, tstop, SpikeTimes, compareTo)
StimSpace = tstop/ StimCount;

    TrialNeurons= zeros(StimCount, neurons);
    for i = 1:size(SpikeTimes, 1)
        trialNo = ceil(SpikeTimes(i, 2) / StimSpace);
        kosor = (SpikeTimes(i, 2) / StimSpace) - (trialNo -1);
        if (kosor < 0.5)
            neuronNo = SpikeTimes(i, 1);
            TrialNeurons(trialNo, neuronNo+1) =         TrialNeurons(trialNo, neuronNo+1) + 1;
    
        end
        
    end

    for n = 1:size(TrialNeurons, 1)

        TrialNeurons(n, :) =     TrialNeurons(n, :)./ norm(    TrialNeurons(n, :));
    end

    cor = [];

    for t = 1:size(TrialNeurons, 1)
        cor(end+1) = dot(TrialNeurons(compareTo, :), TrialNeurons(t, :) );
    end

end

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