Compact implementation of the Izhikevich neuron model

izhikevich.py

import numpy

class IzhikevichNeuron(object):
    def __init__(self, a, b, c, d, v, u = None):
        self.a = a
        self.b = b
        self.c = c
        self.d = d
        self.v = v
        self.u = u if u is not None else b * v
        
class IzhikevichSim(object):
    def __init__(self, nrn, t, dt = 0.25):
        self.neuron = nrn
        self.dt = dt
        self.t = t = numpy.arange(0, t + dt, dt)
        self.stim = numpy.zeros(len(t))
        self.x = 5
        self.y = 140
        self.du = lambda a, b, v, u: a * (b * v - u)
        
    def integrate(self):
        trace = numpy.zeros((2, len(self.t)))
        
        for i in enumerate(self.stim):
            self.neuron.v += self.dt * (0.04 * self.neuron.v ** 2 + self.x * self.neuron.v + self.y - self.neuron.u + self.stim[i[0]])
            self.neuron.u += self.dt * self.du(self.neuron.a, self.neuron.b, self.neuron.v, self.neuron.u)
            
            if self.neuron.v >= 30:
                trace[0, i[0]] = 30
                self.neuron.v = self.neuron.c
                self.neuron.u += self.neuron.d
            else:
                trace[0, i[0]] = self.neuron.v
                trace[1, i[0]] = self.neuron.u
        
        return trace