def scalar_blend(sig_a, sig_b, seed=0):
with nengo.Network() as dd_model:
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)
n_prod = 250
prod_dim = 2
mult_config = nengo.Config(nengo.Ensemble)
mult_config[nengo.Ensemble].encoders = nengo.dists.Choice([[1, 1], [1, -1], [-1, -1], [-1, 1]])
mult_config[nengo.Ensemble].neuron_type = nengo.LIFRate()
mult_config[nengo.Ensemble].radius = np.sqrt(2)
with mult_config:
prod_a = nengo.Ensemble(n_prod, prod_dim)
prod_b = nengo.Ensemble(n_prod, prod_dim)
scale_control = nengo.Node(sw.step)
in_start = nengo.Node(lambda t, x: 1 - x, size_in=1) # start at 0 and go to 1
nengo.Connection(scale_control, in_start, synapse=None)
output = nengo.Node(size_in=1)
dda = get_direct_decoders(sig_a, period, osc, bump_func)
ddb = get_direct_decoders(sig_b, period, osc, bump_func)
bump = nengo.Node(bump_func)
nengo.Connection(bump, osc.ensemble[0])
nengo.Connection(osc.ensemble.neurons, prod_a[0], transform=dda.T)
nengo.Connection(osc.ensemble.neurons, prod_b[0], transform=ddb.T)
nengo.Connection(in_start, prod_a[1])
nengo.Connection(scale_control, prod_b[1])
conn_a = nengo.Connection(prod_a, output, function=lambda x: x[0] * x[1])
conn_b = nengo.Connection(prod_b, output, function=lambda x: x[0] * x[1])
p_in_start = nengo.Probe(in_start)
p_inhib = nengo.Probe(scale_control)
p_a = nengo.Probe(conn_a, synapse=0.01)
p_b = nengo.Probe(conn_b, synapse=0.01)
p_out = nengo.Probe(output)
with nengo.Simulator(dd_model) as dd_sim:
dd_sim.run((t_start + t_blend + 2 * period))
return dd_sim.data[p_a], dd_sim.data[p_b], dd_sim.data[p_out], dd_sim.data[p_inhib], dd_sim.data[p_in_start]
def scalar_blend(sig_a, sig_b, seed=0):
with nengo.Network() as dd_model:
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)
n_out = 100
with osc.config:
out_a = nengo.Ensemble(n_out, 1)
out_b = nengo.Ensemble(n_out, 1)
inhib_control = nengo.Node(sw.step)
in_start = nengo.Node(lambda t, x: x-1, size_in=1) # start at 0 and go to -1
nengo.Connection(inhib_control, in_start, synapse=None)
output = nengo.Node(size_in=1)
dda = get_direct_decoders(sig_a, period, osc, bump_func)
ddb = get_direct_decoders(sig_b, period, osc, bump_func)
bump = nengo.Node(bump_func)
nengo.Connection(bump, osc.ensemble[0])
nengo.Connection(osc.ensemble.neurons, out_a, transform=dda.T)
nengo.Connection(osc.ensemble.neurons, out_b, transform=ddb.T)
nengo.Connection(in_start, out_a.neurons, transform=3*np.ones((n_out, 1)))
nengo.Connection(inhib_control, out_b.neurons, transform=-3*np.ones((n_out, 1)))
nengo.Connection(out_a, output)
nengo.Connection(out_b, output)
p_in_start = nengo.Probe(in_start)
p_inhib = nengo.Probe(inhib_control)
p_a = nengo.Probe(out_a, synapse=0.01)
p_b = nengo.Probe(out_b, synapse=0.01)
p_out = nengo.Probe(output)
with nengo.Simulator(dd_model) as dd_sim:
dd_sim.run((t_start + t_blend + 2*period))
return dd_sim.data[p_a], dd_sim.data[p_b], dd_sim.data[p_out], dd_sim.data[p_inhib], dd_sim.data[p_in_start]
xv = np.linspace(0, 2 * np.pi, 100)
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 target_func(t):
return np.sin(sin_per*t)
def bump_func(t):
return 1 if t < 0.1 else 0
with nengo.Network() as dd_model:
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, 300, net=osc)
output = nengo.Node(size_in=1)
dd = get_direct_decoders(target_func, period, osc, bump_func)
bump = nengo.Node(bump_func)
nengo.Connection(bump, osc.ensemble[0])
nengo.Connection(osc.ensemble.neurons, output, transform=dd.T)
p_out = nengo.Probe(output)
with nengo.Simulator(dd_model) as dd_sim:
dd_sim.run(3*period)
plt.plot(dd_sim.data[p_out][int(2*period/dt):])
plt.show()
