pyeeg.simulate.JansenRit

class pyeeg.simulate.JansenRit(dt=0.0001, seed=42, nonlinearity=<function sigmoid>)

Jansen-Rit model.

3 populations: excitatory, inhibitory and pyramidal:

___________    ___________
|         |    |         |
!  Inhib  !    !  Excit  !
|         |    |         |
-----------    -----------
C2,C4 \             / C1, C3
       \___________/
        |         |
        ! Pyramid !
        |         |
        -----------

Parameters and typical values as in Grimbert & Faugeras, 2006:

  • C1, C2, C3, C4: Average number of synapses between populations: 135 * [1, 0.8, 0.25, 0.25]

  • tau_e: Time scale for excitatory population: 100 ms

  • tau_i: Time scale for inhibitory population: 50 ms

  • G_exc: Average excitatory synaptic gain: 3.25

  • G_inh: Average inhibitory synaptic gain: 22

  • rmax: Amplitude of sigmoid: 5 s^-1

  • beta: Slope of sigmoid: 0.56 mV^-1

  • theta: Threshold of sigmoid: 6 mV

  • Conduction velocity: 10 m/s

  • h: Integration time step: 0.0001 s (by default)

  • P: External input to each of the neural masses: 150 Hz (constant input)

  • Coupling: Coupling between the neural masses: [0.1:0.012:0.292]

This table is from the paper: Kulik et al, Network Neurosci. (2023)

Methods

JansenRit.read_out()

JansenRit.simulate([x0, tmax, noise, P])

Simulate the Jansen-Rit model and monitor the output.

JansenRit.step([I])

Compute one step of the Jansen-Rit model.