... spontaneously active ? no structure, no noise, just heterogeneity. Basically just two populations (e&i) of Izh spike frequency adaptation neurons with heterogeneous parameters. Without heterogeneity the system is... less interesting ( regular spikes or no spikes at all ). Heterogeneous parameters may be better than injected noise in some situations ?

class CustomNeuron(Model):

def __init__(self,model_id=0,a=None,b=None,c=None,d=None,name="Izhikevich",noise=False):

Model.__init__(self,name)

newVar(self,'a','',lambda n:numpy.array(uniform(0.009,0.011)(n)).astype(numpy.float32))

newVar(self,'b','',lambda n:numpy.array(uniform(0.19,0.21)(n)).astype(numpy.float32))

newVar(self,'c','',lambda n:numpy.array(uniform(-70.0,-60.0)(n)).astype(numpy.float32))

newVar(self,'d','',lambda n:numpy.array(uniform(7.0,9.0)(n)).astype(numpy.float32))

newVar(self,'v',deqn('v','DT*(0.04*v*v+5*v+140-u+input_current)'),

lambda n:numpy.array(uniform(-50,-40)(n)).astype(numpy.float32))

newVar(self,'u',deqn('u','a*(b*v-u)'),lambda n:numpy.zeros(n).astype(numpy.float32))

self.spike_condition = 'v>30'

self.reset = ('d_v[idx_state] = c; d_u[idx_state] = u+d;')

self.dont_reset = ''

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