Adaptive Exponential Integrate-and-Fire model

Adaptive Exponential Integrate-and-Fire neuron model is a spiking model, describes single neuron behavior and can generate many kinds of firing patterns by tuning parameters.

[1]:

import brainpy as bp
import brainmodels

bp.math.set_dt(0.02)

Tonic

[2]:

group = brainmodels.neurons.AdExIF(size=1, a=0., b=60., R=.5, delta_T=2., tau=20., tau_w=30.,
                                   V_reset=-55., V_rest=-70, V_th=-30, V_T=-50,
                                   monitors=['V', 'w'])
group.run(500., inputs=('input', 65.))
fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, ylabel='V', title='tonic')
fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.w, ylabel='w', show=True)

Adapting

[3]:

group = brainmodels.neurons.AdExIF(size=1, a=0., b=5., R=.5, tau=20., tau_w=100., delta_T=2.,
                                   V_reset=-55., V_rest=-70, V_th=-30, V_T=-50,
                                   monitors=['V', 'w'], )
group.run(200., inputs=('input', 65.))
fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, ylabel='V', title='adapting')
fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.w, ylabel='w', show=True)

Init Bursting

[4]:

group = brainmodels.neurons.AdExIF(size=1, a=.5, b=7., R=.5, tau=5., tau_w=100., delta_T=2.,
                                   V_reset=-51, V_rest=-70, V_th=-30, V_T=-50,
                                   monitors=['V', 'w'])
group.run(300, inputs=('input', 65))
fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, ylabel='V', ylim=(-55., -35.), title='init_bursting')
fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.w, ylabel='w', show=True)

Bursting

[5]:

group = brainmodels.neurons.AdExIF(size=1, a=-0.5, b=7., R=.5, delta_T=2., tau=5, tau_w=100,
                                   V_reset=-46, V_rest=-70, V_th=-30, V_T=-50,
                                   monitors=['V', 'w'])
group.run(500, inputs=('input', 65))
fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, ylabel='V', ylim=(-60., -35.), title='bursting')
fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.w, ylabel='w', show=True)

../_images/neurons_AdExIF_model_10_0.png

Transient

[6]:

group = brainmodels.neurons.AdExIF(size=1, a=1., b=10., R=.5, tau=10, tau_w=100, delta_T=2.,
                                   V_reset=-60, V_rest=-70, V_th=-30, V_T=-50,
                                   monitors=['V', 'w'])
group.run(500, inputs=('input', 55))
fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, ylabel='V', ylim=(-65., -35.), title='transient')
fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.w, ylabel='w', show=True)

../_images/neurons_AdExIF_model_12_0.png

Delayed

[7]:

group = brainmodels.neurons.AdExIF(size=1, a=-1., b=10., R=.5, delta_T=2., tau=5., tau_w=100.,
                                   V_reset=-60, V_rest=-70, V_th=-30, V_T=-50,
                                   monitors=['V', 'w'])
group.run(500, inputs=('input', 20.))
fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, ylabel='V', ylim=(-65., -35.), title='delayed')
fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.w, ylabel='w', show=True)

../_images/neurons_AdExIF_model_14_0.png