hetero=-2,
integration_rate=0.01,
name='WORDS_HIDDEN')
# Response layer, responses: ('red', 'green')
response_layer = pnl.RecurrentTransferMechanism(size=2,
function=pnl.Logistic(),
hetero=-2.0,
integrator_mode=True,
integration_rate=0.01,
output_ports = [pnl.RESULT,
{pnl.NAME: 'DECISION_ENERGY',
pnl.VARIABLE: (pnl.OWNER_VALUE,0),
pnl.FUNCTION: pnl.Stability(
default_variable = np.array([0.0, 0.0]),
metric = pnl.ENERGY,
matrix = np.array([[0.0, -4.0],
[-4.0, 0.0]]))}],
name='RESPONSE', )
# Mapping projections---------------------------------------------------------------------------------------------------
color_input_weights = pnl.MappingProjection(matrix=np.array([[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]]))
word_input_weights = pnl.MappingProjection(matrix=np.array([[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]]))
task_input_weights = pnl.MappingProjection(matrix=np.array([[1.0, 0.0],
{
pnl.NAME: 'REWARD RATE',
# pnl.VARIABLE: [pnl.OWNER_VALUE],
pnl.VARIABLE: [(pnl.OWNER_VALUE, 0)],
pnl.FUNCTION: pnl.AdaptiveIntegrator(rate=0.2)
},
{
pnl.NAME:
'CONFLICT K',
# pnl.VARIABLE: [pnl.OWNER_VALUE],
pnl.VARIABLE: [(pnl.OWNER_VALUE, 0)],
#Jon said this should also work and would be safer: [(pnl.OWNER_VALUE, 0),
#(pnl.OWNER_VALUE, 1)], but it doesn't work (maybe I did sth wrong)
pnl.FUNCTION:
pnl.Stability(default_variable=[0, 0],
metric=pnl.ENERGY,
normalize=True)
},
],
#as stated in the paper 'Response conflict was calculated as a normalized measure of the energy in the response units during the trial'
name='Action Selection')
# rate = pnl.ObjectiveMechanism(monitored_output_states=[action_selection.output_states[0]],
# function=pnl.AdaptiveIntegrator(rate=0.2,
# noise=reward,
# time_step_size=0.02),
# name='REWARD RATE')
# K = pnl.ObjectiveMechanism(#size=1,
# monitored_output_states=[action_selection.output_state],
# function=pnl.Stability(metric=pnl.ENERGY,
#should be randomly distributed noise to the net input of each unit (except input unit)
# Now a RecurrentTransferMechanism compared to Lauda's Stroop model!
response_layer = pnl.RecurrentTransferMechanism(
size=2, #Recurrent
function=pnl.
Logistic, #pnl.Stability(matrix=np.matrix([[0.0, -1.0], [-1.0, 0.0]])),
name='RESPONSE',
output_states=[
pnl.RECURRENT_OUTPUT.RESULT, {
pnl.NAME:
'DECISION_ENERGY',
pnl.VARIABLE: (pnl.OWNER_VALUE, 0),
pnl.FUNCTION:
pnl.Stability(default_variable=np.array([0.0, -1.0]),
metric=pnl.ENERGY,
matrix=np.array([[0.0, -1.0], [-1.0, 0.0]]))
}
],
integrator_mode=True, #)
# noise=pnl.NormalDist(mean=0.0, standard_dev=.01).function)
smoothing_factor=0.1)
#response_layer.set_log_conditions('value')
#response_layer.set_log_conditions('gain')
# SET UP CONNECTIONS
# rows correspond to sender
# columns correspond to: weighting of the contribution that a given sender makes to the receiver
# in linear algebra terms can write out the matrix
# Input to hidden
def test_Stability_squeezes_variable():
s1 = pnl.Stability(default_variable=[[0.0, 0.0]])
s2 = pnl.Stability(default_variable=[0.0, 0.0])
assert s1.execute([5, 5]) == s2.execute([5, 5])