def w_conc_gen(self, n):
w = np.dot(conceptors[self.count], gen_w_rec(n))
self.count += 1
return w
sin_per = (2 * np.pi * 10) / t_len
cos_per = (2 * np.pi * 20) / t_len
sigs = [
lambda t: np.sin(sin_per*t),
lambda t: 0.5*np.cos(cos_per*t),
]
n_sigs = len(sigs)
apert = np.ones(n_sigs) * 10
rate_data = np.zeros((n_sigs, t_steps, n_neurons))
pat_data = np.zeros((n_sigs, t_steps))
init_x = np.zeros((n_sigs, n_neurons))
w_rec = gen_w_rec(n_neurons)
#neuron_type = nengo.LIFRate()
neuron_type = TanhWithBias(seed=SEED)
for i_s, sig in enumerate(sigs):
# get the rate data
with nengo.Network() as rate_acc:
in_sig = nengo.Node(sig)
sig_reserv = nengo.Ensemble(n_neurons, sig_dims, neuron_type=neuron_type, seed=SEED)
nengo.Connection(sig_reserv.neurons, sig_reserv.neurons, transform=w_rec, synapse=0)
nengo.Connection(in_sig, sig_reserv, synapse=None)
p_rate = nengo.Probe(sig_reserv.neurons, synapse=None)
p_pat = nengo.Probe(in_sig)
with nengo.Simulator(rate_acc) as rate_sim:
# TODO: make sure these are actually periodic
sigs = [
lambda t: np.sin(20*t),
lambda t: 0.5*np.cos(80*t),
lambda t: funky_sig(t)
]
n_sigs = len(sigs)
apert = np.ones(n_sigs) * 10
t_len = t_period*t_scale
t_steps = int(t_len / dt)
rate_data = np.zeros((n_sigs, t_len))
pat_data = np.zeros((n_sigs, t_len))
w_rec_base = gen_w_rec(n_neurons)
def make_div_net(origin, w_func):
sig_ens = []
with nengo.Network as d_n:
for s_i in range(n_sigs):
sig_ens.append(nengo.Ensemble(n_neurons, 1, seed=SEED))
nengo.Connection(origin.neurons, sig_ens[-1].neurons, transform=w_rec_base)
nengo.Connection(sig_ens[-1].neurons, origin.neurons, transform=w_func(n_neurons))
return d_n, sig_ens
for i_s, sig in enumerate(sigs):
# get the rate data
with nengo.Network() as rate_acc:
in_sig = nengo.Node(sig)
