# Create mechanisms ---------------------------------------------------------------------------------------------------
# Input Layer --- [ Target, Distractor ]
input_layer = pnl.TransferMechanism(size=2,
initial_value=np.array([[0.0, 0.0]]),
name='INPUT LAYER')
# Create Decision Layer --- [ Target, Distractor ]
decision_layer = pnl.LCA(
size=2,
time_step_size=dt,
leak=-1.0,
self_excitation=w_XiXi,
competition=w_XiXj,
# Recurrent matrix: [ w_XiXi -w_XiXj ]
# [ -w_XiXj w_XiXi ]
function=pnl.Logistic(bias=b_decision),
noise=pnl.NormalDist(standard_dev=SD).function,
integrator_mode=True,
name='DECISION LAYER')
# Create Response Layer --- [ Target ]
response_layer = pnl.LCA(
size=1,
time_step_size=dt,
leak=-1.0,
self_excitation=w_X3X3,
# Recurrent matrix: [w_X3X3]
# Competition param does not apply because there is only one unit
# WEIGHTS TO COME FROM SEBASTIAN
example_stimulus_inputs = [[1, 0, 0, 1], [1, 0, 1, 0]]
example_task_inputs = [[0, 0, 0, 1], [1, 0, 0, 0]]
example_training_pattern = [[0, 0, 0, 1], [1, 0, 0, 0]]
# RUN THIS TO GET SPACE OF INPUTS ON WHICH TO OPTIMIZE LCA PARAMS:
inputs_to_LCA = multitasking_system.run(inputs={
stimulus_layer: example_stimulus_inputs,
task_layer: example_task_inputs
})
# SOME PYTHON ALGORITHM HERE THAT SELECTS THE 2-UNIT SUBVECTOR FROM inputs_to_LCA CORRESPONDING TO THE RELEVANT TASK
# AS INPUT TO optimization_system BELOW, AND THEN RUN THE SYSTEM FOR EACH INPUT, USING EVC TO OPTIMIZE LCA PARAMETERS
# FOR EACH, BASED CONTROL PARAMETERS AND OBJECTIVE FUNCTION
input_layer = pnl.TransferMechanism(size=2)
decision_layer = pnl.LCA(
size=2,
# INCLUDE TERMINATION CONDITION USING THREHSOLD = ControlSignal)
)
decision_process = pnl.Process(input_layer, decision_layer)
optimization_system = pnl.System(
processes=decision_process,
monitor_for_control=[decision_layer.output_states[pnl.RESULT]])
# ADD COMPARATOR MECHANISM FOR ACCURACY DETERMINATION
# EVC PARAMS:
# - number of simulations to run per LCA
# - which inputs to provide (i.e., *NOT* using typical PredictionMechanisms)
# Input Layer --- [ Target 1, Target 2, Distractor ]
# First, we create the 3 layers of the behavioral network, i.e. INPUT LAYER, DECISION LAYER, and RESPONSE LAYER.
input_layer = pnl.TransferMechanism(
size=3, # Number of units in input layer
initial_value=[[0.0, 0.0, 0.0]], # Initial input values
name='INPUT LAYER' # Define the name of the layer; this is optional,
) # but will help you to overview your model later on
# Create Decision Layer --- [ Target 1, Target 2, Distractor ]
decision_layer = pnl.LCA(
size=3, # Number of units in input layer
initial_value=[[0.0, 0.0, 0.0]], # Initial input values
time_step_size=dt, # Integration step size
leak=-1.0, # Sets off diagonals to negative values
self_excitation=selfdwt, # Set diagonals to self excitate
competition=inhwt, # Set off diagonals to inhibit
function=pnl.Logistic(bias=decbias), # Set the Logistic function with bias = decbias
# noise=pnl.UniformToNormalDist(standard_dev = SD).function, # The UniformToNormalDist function will
integrator_mode=True, # set the noise with a seed generator that is compatible with
name='DECISION LAYER' # MATLAB random seed generator 22 (rsg=22)
)
# decision_layer.set_log_conditions('RESULT') # Log RESULT of the decision layer
decision_layer.set_log_conditions('value') # Log value of the decision layer
for output_state in decision_layer.output_states:
output_state.value *= 0.0 # Set initial output values for decision layer to 0
# Create Response Layer --- [ Target1, Target2 ]
response_layer = pnl.LCA(
size=2, # Number of units in input layer