def model(self, p):
model = spa.SPA()
with model:
model.vision = spa.Buffer(dimensions=p.D)
model.phrase = spa.Buffer(dimensions=p.D)
model.motor = spa.Buffer(dimensions=p.D)
model.noun = spa.Memory(dimensions=p.D, synapse=0.1)
model.verb = spa.Memory(dimensions=p.D, synapse=0.1)
model.bg = spa.BasalGanglia(
spa.Actions(
'dot(vision, WRITE) --> verb=vision',
'dot(vision, ONE+TWO+THREE) --> noun=vision',
'0.5*(dot(vision, NONE-WRITE-ONE-TWO-THREE) + '
'dot(phrase, WRITE*VERB))'
'--> motor=phrase*~NOUN',
))
model.thal = spa.Thalamus(model.bg)
model.cortical = spa.Cortical(
spa.Actions(
'phrase=noun*NOUN',
'phrase=verb*VERB',
))
def vision_input(t):
index = int(t / p.time_per_word) % 3
return ['WRITE', 'ONE', 'NONE'][index]
model.input = spa.Input(vision=vision_input)
self.motor_vocab = model.get_output_vocab('motor')
self.p_thal = nengo.Probe(model.thal.actions.output, synapse=0.03)
self.p_motor = nengo.Probe(model.motor.state.output, synapse=0.03)
split.split_input_nodes(model, max_dim=64)
self.replaced = split.split_passthrough(model, max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[nengo_spinnaker.Simulator].placer_kwargs = dict(
effort=0.1)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def model(self, p):
model = spa.SPA()
with model:
model.vision = spa.Buffer(dimensions=p.D)
model.phrase = spa.Buffer(dimensions=p.D)
model.motor = spa.Buffer(dimensions=p.D)
model.noun = spa.Memory(dimensions=p.D, synapse=0.1)
model.verb = spa.Memory(dimensions=p.D, synapse=0.1)
model.bg = spa.BasalGanglia(spa.Actions(
'dot(vision, WRITE) --> verb=vision',
'dot(vision, ONE+TWO+THREE) --> noun=vision',
'0.5*(dot(vision, NONE-WRITE-ONE-TWO-THREE) + '
'dot(phrase, WRITE*VERB))'
'--> motor=phrase*~NOUN',
))
model.thal = spa.Thalamus(model.bg)
model.cortical = spa.Cortical(spa.Actions(
'phrase=noun*NOUN',
'phrase=verb*VERB',
))
def vision_input(t):
index = int(t / p.time_per_word) % 3
return ['WRITE', 'ONE', 'NONE'][index]
model.input = spa.Input(vision=vision_input)
self.motor_vocab = model.get_output_vocab('motor')
self.p_thal = nengo.Probe(model.thal.actions.output, synapse=0.03)
self.p_motor = nengo.Probe(model.motor.state.output, synapse=0.03)
split.split_input_nodes(model, max_dim=64)
self.replaced = split.split_passthrough(model,
max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[
nengo_spinnaker.Simulator].placer_kwargs = dict(effort=0.1)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def model(self, p):
model = spa.SPA()
with model:
model.vision = spa.Buffer(dimensions=p.D)
model.state = spa.Memory(dimensions=p.D)
actions = [
'dot(state, S%d) --> state=S%d' % (i, (i + 1))
for i in range(p.n_actions - 1)
]
actions.append('dot(state, S%d) --> state=vision' %
(p.n_actions - 1))
model.bg = spa.BasalGanglia(actions=spa.Actions(*actions))
model.thal = spa.Thalamus(model.bg)
model.input = spa.Input(vision='S%d' % p.start,
state=lambda t: 'S%d' % p.start
if t < 0.1 else '0')
self.probe = nengo.Probe(model.thal.actions.output, synapse=0.03)
split.split_passthrough(model, max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[nengo_spinnaker.Simulator].placer_kwargs = dict(
effort=0.1)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def model(self, p):
model = spa.SPA()
with model:
model.word = spa.State(dimensions=p.D)
model.marker = spa.State(dimensions=p.D)
model.memory = spa.State(dimensions=p.D, feedback=1)
model.cortical = spa.Cortical(spa.Actions(
'memory = word * marker',
))
def word(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'S%d' % index
return '0'
def marker(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'M%d' % index
return '0'
model.input = spa.Input(word=word, marker=marker)
self.p_memory = nengo.Probe(model.memory.output, synapse=0.03)
self.vocab = model.get_output_vocab('memory')
split.split_input_nodes(model, max_dim=16)
self.replaced = split.split_passthrough(model,
max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[
nengo_spinnaker.Simulator].placer_kwargs = dict(effort=0.1)
split.remove_outputless_passthrough(model)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def model(self, p):
model = spa.SPA()
with model:
model.vision = spa.Buffer(dimensions=p.D)
model.state = spa.Memory(dimensions=p.D)
actions = ['dot(state, S%d) --> state=S%d' % (i,(i+1))
for i in range(p.n_actions - 1)]
actions.append('dot(state, S%d) --> state=vision' %
(p.n_actions - 1))
model.bg = spa.BasalGanglia(actions=spa.Actions(*actions))
model.thal = spa.Thalamus(model.bg)
model.input = spa.Input(vision='S%d' % p.start,
state=lambda t: 'S%d' % p.start if
t < 0.1 else '0')
self.probe = nengo.Probe(model.thal.actions.output, synapse=0.03)
split.split_passthrough(model, max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[nengo_spinnaker.Simulator].placer_kwargs = dict(effort=0.1)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def model(self, p):
model = spa.SPA()
with model:
model.word = spa.State(dimensions=p.D)
model.marker = spa.State(dimensions=p.D)
model.memory = spa.State(dimensions=p.D, feedback=1)
model.cortical = spa.Cortical(
spa.Actions('memory = word * marker', ))
def word(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'S%d' % index
return '0'
def marker(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'M%d' % index
return '0'
model.input = spa.Input(word=word, marker=marker)
self.p_memory = nengo.Probe(model.memory.output, synapse=0.03)
self.vocab = model.get_output_vocab('memory')
split.split_input_nodes(model, max_dim=16)
self.replaced = split.split_passthrough(model, max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[nengo_spinnaker.Simulator].placer_kwargs = dict(
effort=0.1)
split.remove_outputless_passthrough(model)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model