def switch_to_initialization_trial(mechanisms):
# Turn off accumulation
switch_integrator_mode(mechanisms, False)
# Turn off noise
switch_noise(mechanisms, 0)
# Execute once per trial
my_Stroop.termination_processing = {pnl.TimeScale.TRIAL: pnl.AllHaveRun()}
def _switch_trial_settings(self,
integrator_mode=False,
noise=0,
termination: pnl.Condition = pnl.AllHaveRun(),
learning=False):
self._switch_integrator_mode(integrator_mode)
self._switch_noise(noise)
self.system.termination_processing = {pnl.TimeScale.TRIAL: termination}
for learning_mechanism, learning_rate in self.learning_mechanisms_to_learning_rates.items(
):
learning_mechanism.learning_rate = learning and learning_rate or 0
Inner_Composition.add_node(E)
Inner_Composition.add_node(F)
Inner_Composition.add_projection(
projection=pnl.MappingProjection(
name="MappingProjection from E[RESULT] to F[InputPort-0]",
function=pnl.LinearMatrix(matrix=[[1.0]]),
),
sender=E,
receiver=F,
)
Inner_Composition.scheduler.termination_conds = {
pnl.TimeScale.RUN: pnl.Never(),
pnl.TimeScale.TRIAL: pnl.AllHaveRun(),
}
comp.add_node(A)
comp.add_node(B)
comp.add_node(C)
comp.add_node(D)
comp.add_node(Inner_Composition)
comp.add_projection(
projection=pnl.MappingProjection(
name="MappingProjection from A[RESULT] to B[InputPort-0]",
function=pnl.LinearMatrix(matrix=[[1.0]], default_variable=[2.0]),
),
sender=A,
receiver=B,
)
Stroop_model.add_projection(
projection=pnl.MappingProjection(
name="MappingProjection_from_TASK_RESULT__to_word_hidden_InputPort_0",
function=pnl.LinearMatrix(default_variable=[0.5, 0.5],
matrix=[[0.0, 0.0], [4.0, 4.0]]),
),
sender=TASK,
receiver=word_hidden,
)
Stroop_model.add_controller(CONTROL)
Stroop_model.scheduler.add_condition(word_hidden,
pnl.EveryNCalls(dependency=TASK, n=10))
Stroop_model.scheduler.add_condition(color_hidden,
pnl.EveryNCalls(dependency=TASK, n=10))
Stroop_model.scheduler.add_condition(
OUTPUT,
pnl.All(
pnl.EveryNCalls(dependency=color_hidden, n=1),
pnl.EveryNCalls(dependency=word_hidden, n=1),
),
)
Stroop_model.scheduler.add_condition(DECISION,
pnl.EveryNCalls(dependency=OUTPUT, n=1))
Stroop_model.scheduler.termination_conds = {
pnl.TimeScale.ENVIRONMENT_SEQUENCE: pnl.Never(),
pnl.TimeScale.ENVIRONMENT_STATE_UPDATE: pnl.AllHaveRun(),
}
import psyneulink as pnl
comp = pnl.Composition(name='comp')
fn = pnl.IntegratorMechanism(name='fn', function=pnl.FitzHughNagumoIntegrator(name='FitzHughNagumoIntegrator Function-0', d_v=1, initial_v=-1, initializer=[1.0], default_variable=[[0]]))
comp.add_node(fn)
comp.scheduler.add_condition(fn, pnl.Always())
comp.scheduler.termination_conds = {pnl.TimeScale.RUN: pnl.Never(), pnl.TimeScale.TRIAL: pnl.AllHaveRun()}
comp.show_graph()