def trial(network: lb.Net, input_pattern: Iterable[float],
output_pattern: Iterable[float]) -> None:
"""Runs a trial."""
network.clamp_layer("input", input_pattern)
network.minus_phase_cycle(num_cycles=50)
network.clamp_layer("output", output_pattern)
network.plus_phase_cycle(num_cycles=25)
network.unclamp_layer("input")
network.unclamp_layer("output")
network.learn()
def trial(network: lb.Net, input_pattern: Iterable[float],
output_pattern: Iterable[float]) -> None:
"""Runs a trial."""
network.clamp_layer("input", input_pattern)
network.phase_cycle(phase=lb.MinusPhase, num_cycles=50)
network.clamp_layer("output", output_pattern)
network.phase_cycle(phase=lb.PlusPhase, num_cycles=25)
network.unclamp_layer("input")
network.unclamp_layer("output")
network.end_trial()
def learn_trial(network: lb.Net, pattern=torch.Tensor) -> None:
# Theta Trough
network.clamp_layer("Input", pattern)
network.inhibit_projns("TSP: CA3 -> CA1", "Loop: EC_out -> EC_in")
network.phase_cycle(ThetaTrough, num_cycles=20)
network.uninhibit_projns("TSP: CA3 -> CA1", "Loop: EC_out -> EC_in")
# Theta Peak
network.inhibit_projns("MSP: EC_in -> CA1", "Loop: EC_out -> EC_in")
network.phase_cycle(ThetaPeak, num_cycles=20)
network.uninhibit_projns("MSP: EC_in -> CA1", "Loop: EC_out -> EC_in")
# Theta Plus
network.clamp_layer("EC_out", pattern)
network.inhibit_projns("TSP: CA3 -> CA1")
network.phase_cycle(ThetaPlus, num_cycles=60)
network.inhibit_projns("TSP: CA3 -> CA1")
# Reset
network.unclamp_layer("Input", "EC_out")
network.end_trial()
network.learn()