inter_arc = interpolate.interp1d(xv, np.arctan2(np.cos(xv), np.sin(xv)))
def arc_func(x):
return inter_arc(2 * np.pi * x / period % (2 * np.pi))
with nengo.Network() as ad_model:
bump = nengo.Node(bump_func)
osc = nengo.Network()
osc.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
osc.config[nengo.Ensemble].seed = seed
nengo.networks.Oscillator(0.1, 2 * np.pi / period, 300, net=osc)
dd = get_direct_decoders(arc_func, period, osc, bump_func)
dmp, conn_func = make_attrac_net(proc_func, 300, dd=dd, seed=seed)
nengo.Connection(bump, osc.ensemble[0])
nengo.Connection(osc.ensemble.neurons, dmp.input, function=conn_func)
p_arc = nengo.Probe(arctan, synapse=0.01)
p_out = nengo.Probe(dmp.output, synapse=0.01)
with nengo.Simulator(ad_model) as ad_sim:
ad_sim.run(4 * period)
plt.plot(ad_sim.data[p_out][int(2 * period / dt) :])
plt.show()
def bump_func(t):
return 1 if t < 0.1 else 0
pre_dat = target_func(np.linspace(0, period, 100))
xv = np.linspace(-np.pi, np.pi, pre_dat.shape[0])
proc_func = interpolate.interp1d(xv, pre_dat)
with nengo.Network() as ad_model:
bump = nengo.Node(bump_func)
osc = nengo.Network()
osc.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
osc.config[nengo.Ensemble].seed = 0
nengo.networks.Oscillator(0.1, 2*np.pi/period, 300, net=osc)
dmp, conn_func = make_attrac_net(proc_func, 300)
nengo.Connection(bump, osc.ensemble[0])
nengo.Connection(osc.ensemble, dmp.input, function=conn_func)
p_out = nengo.Probe(dmp.output, synapse=0.01)
with nengo.Simulator(ad_model) as ad_sim:
ad_sim.run(4*period)
g_dat = ad_sim.data[p_out][int(2*period/dt):]
plt.plot(d3_scale(g_dat))
plt.show()
