In [1]:
%pylab nbagg
from tvb.simulator.lab import *
Populating the interactive namespace from numpy and matplotlib
   INFO  log level set to INFO

Simulation with the reduced Wong-Wang model

Gustavo et al and Hansen et al 2015 used the reduced Wong-Wang model to reproduce certain aspects of human resting state fMRI. This is a 1D model, so we can tune its parameters simply by plotting the derivative as a function of the state variable:

In [2]:
rww = models.ReducedWongWang(a=0.27, w=1.0, I_o=0.3)
S = linspace(0, 1, 50).reshape((1, -1, 1))
C = S * 0.0
dS = rww.dfun(S, C)

figure()
plot(S.flat, dS.flat)
Out[2]:
[<matplotlib.lines.Line2D at 0x19a18748>]

And a short simulation

In [3]:
sim = simulator.Simulator(
    model=rww,
    connectivity=connectivity.Connectivity(load_default=True),
    coupling=coupling.Linear(a=0.5 / 50.0),
    integrator=integrators.EulerStochastic(dt=1, noise=noise.Additive(nsig=1e-5)), 
    monitors=monitors.TemporalAverage(period=1.),
    simulation_length=5e3
).configure()

(time, data), = sim.run()

figure()
plot(time, data[:, 0, :, 0], 'k', alpha=0.1);
WARNING  File 'hemispheres' not found in ZIP.

References

[DPA_2013] Deco Gustavo, Ponce Alvarez Adrian, Dante Mantini, Gian Luca Romani, Patric Hagmann and Maurizio Corbetta. Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations. The Journal of Neuroscience 32(27), 11239-11252, 2013.