Epileptic seizure model with Morris-Lecar neurons (Beverlin and Netoff 2011)

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Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network.
1 . Beverlin B, Kakalios J, Nykamp D, Netoff TI (2012) Dynamical changes in neurons during seizures determine tonic to clonic shift. J Comput Neurosci 33:41-51 [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): Hodgkin-Huxley neuron; Abstract Morris-Lecar neuron;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Epilepsy;
Implementer(s): Beverlin, Bryce ;
function dydt = ML_derivs(t, y, I, coefs)
% function [dydt] = derivsI(t, y, I)
% The derivs function
persistent V;
persistent n;
persistent Ser;
persistent Sef;
V = y(1,:);
n = y(2,:);
Ser = y(3,:);
Sef = y(4,:);

%dXidt = minf(V)*(1-minf(V)); %MaxRand*sqrt(t-TLast)*randn;
%totalSynapticDrive = gEPSP*((Sef - Ser).*(E_EPSP-V)) + gIPSP*((Sif - Sir).*(E_IPSP-V));

dydt = [1.0./coefs.C*(I-coefs.gL.*(V-coefs.EL) - ...
    coefs.gNa.*(1.0./(1.0 + exp((coefs.Vhalfm - V)/coefs.km ))).*(V-coefs.ENa) - ...
    coefs.gK.*n.*(V-coefs.EK) + ...
    (1.0./(1.0+exp((coefs.Vhalfn - V)/coefs.kn ))-n)./exp(-0.07*V-3);...