def build_stdp(model, stdp, rule):
conn = rule.connection
pre_activities = model.sig[get_pre_ens(conn).neurons]['out']
post_activities = model.sig[get_post_ens(conn).neurons]['out']
pre_trace = Signal(np.zeros(pre_activities.size), name="pre_trace")
post_trace = Signal(np.zeros(post_activities.size), name="post_trace")
model.add_op(SimSTDP(
pre_activities,
post_activities,
pre_trace,
post_trace,
model.sig[conn]['weights'],
model.sig[rule]['delta'],
pre_tau=stdp.pre_tau,
post_tau=stdp.post_tau,
alf_p=stdp.alf_p,
alf_n=stdp.alf_n,
beta_p=stdp.beta_p,
beta_n=stdp.beta_n,
max_weight=stdp.max_weight,
min_weight=stdp.min_weight,
learning_rate=stdp.learning_rate,
))
# expose these for probes
model.sig[rule]['pre_trace'] = pre_trace
model.sig[rule]['post_trace'] = post_trace
model.params[rule] = None # no build-time info to return
def build_moja(model, moja, rule):
conn = rule.connection
pre_activities = model.sig[get_pre_ens(conn).neurons]["out"]
post_activities = model.sig[get_post_ens(conn).neurons]["out"]
pre_filtered = build_or_passthrough(model, moja.pre_synapse,
pre_activities)
post_filtered = build_or_passthrough(model, moja.post_synapse,
post_activities)
pos_memristors, \
neg_memristors, \
r_min_noisy, \
r_max_noisy, \
exponent_noisy = initialise_memristors( moja, pre_filtered.shape[ 0 ], post_filtered.shape[ 0 ] )
model.sig[conn]["pos_memristors"] = pos_memristors
model.sig[conn]["neg_memristors"] = neg_memristors
model.add_op(
SimmOja(pre_filtered, post_filtered, moja.beta,
model.sig[conn]["pos_memristors"],
model.sig[conn]["neg_memristors"], model.sig[conn]["weights"],
moja.noise_percentage, moja.gain, r_min_noisy, r_max_noisy,
exponent_noisy, moja.voltage, moja.initial_state))
# expose these for probes
model.sig[rule]["pre_filtered"] = pre_filtered
model.sig[rule]["post_filtered"] = post_filtered
model.sig[rule]["pos_memristors"] = pos_memristors
model.sig[rule]["neg_memristors"] = neg_memristors
def build_stdp(model, stdp, rule):
conn = rule.connection
pre_activities = model.sig[get_pre_ens(conn).neurons]["out"]
post_activities = model.sig[get_post_ens(conn).neurons]["out"]
pre_trace = Signal(np.zeros(pre_activities.size), name="pre_trace")
post_trace = Signal(np.zeros(post_activities.size), name="post_trace")
pre_scale = Signal(np.zeros(model.sig[conn]["weights"].shape),
name="pre_scale")
post_scale = Signal(np.zeros(model.sig[conn]["weights"].shape),
name="post_scale")
model.add_op(
SimSTDP(
pre_activities,
post_activities,
pre_trace,
post_trace,
pre_scale,
post_scale,
model.sig[conn]["weights"],
model.sig[rule]["delta"],
learning_rate=stdp.learning_rate,
pre_tau=stdp.pre_tau,
post_tau=stdp.post_tau,
pre_amp=stdp.pre_amp,
post_amp=stdp.post_amp,
bounds=stdp.bounds,
max_weight=stdp.max_weight,
min_weight=stdp.min_weight,
))
# expose these for probes
model.sig[rule]["pre_trace"] = pre_trace
model.sig[rule]["post_trace"] = post_trace
model.sig[rule]["pre_scale"] = pre_scale
model.sig[rule]["post_scale"] = post_scale
model.params[rule] = None # no build-time info to return
def build_tripletstdp(model, stdp, rule):
conn = rule.connection
pre_activities = model.sig[get_pre_ens(conn).neurons]["out"]
post_activities = model.sig[get_post_ens(conn).neurons]["out"]
pre_trace1 = Signal(np.zeros(pre_activities.size), name="pre_trace1")
post_trace1 = Signal(np.zeros(post_activities.size), name="post_trace1")
pre_trace2 = Signal(np.zeros(pre_activities.size), name="pre_trace2")
post_trace2 = Signal(np.zeros(post_activities.size), name="post_trace2")
model.add_op(
SimTripletSTDP(
pre_activities,
post_activities,
pre_trace1,
post_trace1,
pre_trace2,
post_trace2,
model.sig[rule]["delta"],
learning_rate=stdp.learning_rate,
pre_tau=stdp.pre_tau,
pre_taux=stdp.pre_taux,
post_tau=stdp.post_tau,
post_tauy=stdp.post_tauy,
pre_amp2=stdp.pre_amp2,
pre_amp3=stdp.pre_amp3,
post_amp2=stdp.post_amp2,
post_amp3=stdp.post_amp3,
nearest_spike=stdp.nearest_spike,
))
# expose these for probes
model.sig[rule]["pre_trace1"] = pre_trace1
model.sig[rule]["post_trace1"] = post_trace1
model.sig[rule]["pre_trace2"] = pre_trace2
model.sig[rule]["post_trace2"] = post_trace2
model.params[rule] = None # no build-time info to return