The Virtual Brain Project

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Reduced Wong-Wang model

In this demo, we show how to perform a region level simulation with the reduced Wong-Wang model, using the default connectivity.

Ensure TVB is set up

tvb_setup
[tvb_setup] using Python 2.7 C:UsersmwDownloadsTVB_Distributiontvb_datapython.exe
TVB modules available.

Build simulator

model = py.tvb.simulator.models.ReducedWongWang();
coupling = py.tvb.simulator.coupling.Linear;
conn = py.tvb.datatypes.connectivity.Connectivity(...
    pyargs('load_default', py.True));
noise = py.tvb.simulator.noise.Additive(pyargs('nsig', 1e-4));

sim = py.tvb.simulator.simulator.Simulator(pyargs(...
    'integrator', py.tvb.simulator.integrators.HeunStochastic(...
        pyargs('dt', 0.1, 'noise', noise)),...
    'model', model, ...
    'coupling', coupling, ...
    'connectivity', conn, ...
    'simulation_length', 1000));

configure(sim);

Plot connectivity weights and tract lengths

figure('Position', [500 500 1000 400])
subplot 121, imagesc(np2m(conn.weights)), colorbar, title('Weights')
subplot 122, imagesc(np2m(conn.tract_lengths)), colorbar
title('Tract Lengths (mm)')
matlab/html/../matlab/html/tvb_demo_region_rww_01.png

Run simulation

data = run(sim);

Convert data to MATLAB format

t = np2m(data{1}{1});
y = np2m(data{1}{2});

Plot results

NB Dimensions will be [mode, node, state var, time]:

figure()
plot(t, squeeze(y(1, :, 1, :)), 'k')
ylabel('S(t)')
xlabel('Time (ms)')
title(sprintf('Reduced Wong-Wang, %d Regions', conn.weights.shape{1}*1))
matlab/html/../matlab/html/tvb_demo_region_rww_02.png