(Brette, Romain. 2004) LIF phase locking

Implementation of the paper:

• Brette, Romain. “Dynamics of one-dimensional spiking neuron models.” Journal of mathematical biology 48.1 (2004): 38-56.

Author:

• Chaoming Wang (chao.brain@qq.com)

import brainpy as bp

%matplotlib inline
import matplotlib.pyplot as plt

# set parameters
num = 2000
tau = 100.  # ms
Vth = 1.  # mV
Vr = 0.  # mV
inputs = bp.math.linspace(2., 4., num)

class LIF(bp.NeuGroup):
  def __init__(self, size, **kwargs):
    super(LIF, self).__init__(size, **kwargs)

    self.V = bp.math.Variable(bp.math.zeros(size))
    self.spike = bp.math.Variable(bp.math.zeros(size))
    self.integral = bp.odeint(self.derivative)

  def derivative(self, V, t):
    return (-V + inputs + 2 * bp.math.sin(2 * bp.math.pi * t / tau)) / tau

  def update(self, _t, _dt):
    V = self.integral(self.V, _t)
    self.spike[:] = bp.math.asarray(V >= Vth, dtype=bp.math.float_)
    self.V[:] = bp.math.where(self.spike > 0., Vr, V)

group = LIF(num, monitors=['spike'])
group = bp.math.jit(group)

group.run(duration=5 * 1000., report=0.1)

indices, times = bp.measure.raster_plot(group.mon.spike, group.mon.ts)

# plt.plot((times % tau) / tau, inputs[indices], ',')

spike_phases = (times % tau) / tau
params = inputs[indices]
plt.scatter(x=spike_phases, y=params, c=spike_phases, marker=',', s=0.1, cmap="coolwarm")

plt.xlabel('Spike phase')
plt.ylabel('Parameter (input)')
plt.show()

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