Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)

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Accession:181967
"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. ... The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition..."
Reference:
1 . Cutsuridis V, Poirazi P (2015) A computational study on how theta modulated inhibition can account for the long temporal windows in the entorhinal-hippocampal loop. Neurobiol Learn Mem 120:69-83 [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:
Cell Type(s): Dentate gyrus granule cell; Hippocampus CA1 pyramidal cell; Hippocampus CA3 pyramidal cell; Hippocampus CA3 interneuron basket cell; Dentate gyrus mossy cell; Dentate gyrus basket cell; Dentate gyrus hilar cell; Hippocampus CA1 basket cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron; Hippocampus CA1 bistratified cell; Hippocampus CA1 axo-axonic cell; Hippocampus CA3 axo-axonic cells;
Channel(s): I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I M; I h; I K,Ca; I_AHP;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Temporal Pattern Generation; Spatio-temporal Activity Patterns; Brain Rhythms; Storage/recall;
Implementer(s): Cutsuridis, Vassilis [vcutsuridis at gmail.com];
Search NeuronDB for information about:  Dentate gyrus granule cell; Hippocampus CA1 pyramidal cell; Hippocampus CA3 pyramidal cell; Hippocampus CA3 interneuron basket cell; GabaA; AMPA; NMDA; I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I M; I h; I K,Ca; I_AHP;
% Generate weight matrix through storage of random binary patterns
% with the clipped Hebbian learning rule.
% Weights stored in incoming column order for each target cell.
% BPG 19-8-08

NCELL = 100;  % number of cells (neurons)
NPATT = 1   % number of patterns
SPATT = 20;   % number of active cells per pattern
%PC = 0.5;    % percent connectivity (not normally used)

% FWGT = 'C:\Users\Vassilis\Desktop\Documents\My Research\Current\DG-CA3-CA1 microcircuit model\Code\NEURON\My model\DG-CA3-CA1-network_w_MS_recall_perf\Weights\DGwgtsN100S4P1.dat';   % weights file
% FPATT = 'C:\Users\Vassilis\Desktop\Documents\My Research\Current\DG-CA3-CA1 microcircuit model\Code\NEURON\My model\DG-CA3-CA1-network_w_MS_recall_perf\Weights\DGpattsN100S4P1.dat';   % patterns file
FWGT = 'C:\Users\Vassilis\Desktop\Documents\My Research\Current\DG-CA3-CA1 microcircuit model\Code\NEURON\My model\DG-CA3-CA1-network_w_MS_recall_perf\Weights\CA3wgtsN100S20P1.dat';   % weights file
FPATT = 'C:\Users\Vassilis\Desktop\Documents\My Research\Current\DG-CA3-CA1 microcircuit model\Code\NEURON\My model\DG-CA3-CA1-network_w_MS_recall_perf\Weights\CA3pattsN100S20P1.dat';   % patterns file
% FWGT = 'C:\Users\Vassilis\Desktop\Documents\My Research\Current\DG-CA3-CA1 microcircuit model\Code\NEURON\My model\DG-CA3-CA1-network_w_MS_recall_perf\Weights\CA1wgtsN100S20P1.dat';   % weights file
% FPATT = 'C:\Users\Vassilis\Desktop\Documents\My Research\Current\DG-CA3-CA1 microcircuit model\Code\NEURON\My model\DG-CA3-CA1-network_w_MS_recall_perf\Weights\CA1pattsN100S20P1.dat';   % patterns file


%rand('state',0);
rand('state',sum(100*clock));

%rw = rand(NCELL);
%w = ones(NCELL).*PC >= rw

w = zeros(NCELL);
p = zeros(NCELL, NPATT);

for i=1:NPATT
  % generate pattern
  pr = randperm(NCELL);
  pi = pr(1:SPATT);        % indices of active cells in pattern
  p(pi,i) = 1;
  % store in weight matrix
  w = w+(p(:,i)*p(:,i)');
end

w = w > 0;  % clip weight matrix

dlmwrite(FWGT, w, ' ');
dlmwrite(FPATT, p, ' ');