def main():
TIMESTEPS = 30
net = nxsdk.net.net.NxNet()
state_machine = StateMachine(net)
out_groups = create_simulated_input(net, TIMESTEPS)
state_machine.connect_in(out_groups)
net.run(TIMESTEPS)
net.disconnect()
#create_visualization(state_machine.behaviors, state_machine.behavior_dictionary, TIMESTEPS)
behavior_names = [
"look", "state_machine.look_at_object",
"state_machine.recognize_object", "state_machine.learn_new_object",
"state_machine.dummy", "state_machine.recognize_object", "query",
"state_machine.query_memory"
]
plotter = Plotter()
for name in behavior_names:
plotter.plot_behavior(state_machine.behavior_dictionary[name])
from dft_loihi.inputs.simulated_input import HomogeneousPiecewiseStaticInput
from dft_loihi.dft.util import connect
# set up the network
net = nxsdk.net.net.NxNet()
neurons_per_node = 1
simulated_input = HomogeneousPiecewiseStaticInput("input", net,
neurons_per_node)
simulated_input.add_spike_rate(0, 100)
simulated_input.add_spike_rate(500, 100)
simulated_input.add_spike_rate(2000, 100)
simulated_input.add_spike_rate(500, 100)
simulated_input.add_spike_rate(0.0, 100)
simulated_input.create()
node = Node("node", net, self_excitation=1.1)
connect(simulated_input, node, 1.1)
# run the network
time_steps = 500
net.run(time_steps)
net.disconnect()
# plot results
plotter = Plotter()
plotter.add_input_plot(simulated_input)
plotter.add_node_plot(node)
plotter.plot()