In [80]:

    

import nengo
import numpy as np


model = nengo.Network()
with model:
    
    
    a = nengo.Ensemble(n_neurons=100, dimensions=1, neuron_type=nengo.AdaptiveLIF(tau_n=0.1, inc_n=0.1))
    b = nengo.Ensemble(n_neurons=100, dimensions=1, neuron_type=nengo.LIF())
    stimulus = nengo.Node(lambda t: 0 if t < 0.1 else 0.5)
    stim_ens = nengo.Ensemble(n_neurons=100, dimensions=1)
    
    tau_input = 0.01
    nengo.Connection(stimulus, stim_ens, synapse=None)
    nengo.Connection(stim_ens, a, synapse=tau_input)
    nengo.Connection(stim_ens, b, synapse=tau_input)
    
    tau_probe = 0.1
    p_a = nengo.Probe(a, synapse=tau_probe)
    p_b = nengo.Probe(b, synapse=0.03)
    p_a_n = nengo.Probe(a.neurons)
    p_b_n = nengo.Probe(b.neurons)
    
    
sim = nengo.Simulator(model)
sim.run(0.3)

plot(sim.trange(), sim.data[p_a]*2.7, label='ALIF')
plot(sim.trange(), sim.data[p_b], label='LIF')
legend(loc='best')
show()

print 'ALIF', sum(sim.data[p_a_n])
print 'LIF', sum(sim.data[p_b_n])


    

    













    




ALIF 1301.0
LIF 2851.0

In [34]:

    




    

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