nengo-forth_test

# based on https://www.nengo.ai/nengo/examples/basic/2d-representation.html
import matplotlib.pyplot as plt
import numpy as np
import nengo
from nengo.dists import Uniform
from nengo.utils.matplotlib import rasterplot


model = nengo.Network(label="2D representation")

with model:
    neurons = nengo.Ensemble(
        100, #number of neurons
        dimensions=2,  # Represent a 2-dimensional signal (input or output?)
        # Set intercept to 0.5
        #intercepts=Uniform(-0.5, -0.5),
        # Set the maximum firing rate of the neuron to 100hz
        #max_rates=Uniform(100, 100),
        # Set the neuron's firing rate to increase for positive input
        #encoders=[[1], [-1]], # One 'on' and one 'off' neuron
    )

    #****************
    # PROVIDE INPUT
    #****************
    #input_signal = nengo.Node(lambda t: np.cos(8 * t))
    sin = nengo.Node(output=np.sin)
    cos = nengo.Node(output=np.cos)

    #****************
    # CONNECT ELEMENTS
    #****************
    #Connect the input signal to the neuron
    # The indices in neurons define which dimension the input will project to

    #nengo.Connection(input_signal, A, synapse=0.01)
    nengo.Connection(sin, neurons[0])
    nengo.Connection(cos, neurons[1])

    #****************
    # PROBES
    #****************
    # The original input
    #input_signal_probe = nengo.Probe(input_signal)
    sin_probe = nengo.Probe(sin, "output")
    cos_probe = nengo.Probe(cos, "output")

    # The raw spikes from the neuron
    neurons_rawspikes_probe = nengo.Probe(neurons.neurons)
    # Subthreshold soma voltage of the neuron
    #voltage = nengo.Probe(A.neurons, "voltage")
    # Spikes filtered by a 10ms post-synaptic filter
    #filtered = nengo.Probe(A.neurons, synapse=0.01)
    #filtered = nengo.Probe(A, synapse=0.01)
    neurons_probe = nengo.Probe(neurons, "decoded_output", synapse=0.01)



with nengo.Simulator(model) as sim:  # Create the simulator
    #****************
    # Run
    #****************
    sim.run(5)  # Run it for 1 second

#****************
# plot the results
#****************
t =  sim.trange()
# Plot the decoded output of the ensemble
plt.figure()
plt.plot(sim.trange(), sim.data[neurons_probe], label="A_output")
#plt.plot(sim.trange(), sim.data[input_signal_probe])
plt.plot(sim.trange(), sim.data[sin_probe])
plt.plot(sim.trange(), sim.data[cos_probe])
#plt.xlim(0, 1)

#plt.plot(sim.trange(), sim.data[neurons_rawspikes_probe])


# Plot the spiking output of the ensemble
#plt.figure()
#rasterplot(sim.trange(), sim.data[neurons_rawspikes_probe])
#plt.xlim(0, 1)


# Plot the spiking output of the ensemble
# plt.figure(figsize=(10, 8))
# plt.subplot(221)
# rasterplot(sim.trange(), sim.data[spikes])
# plt.yticks((1,2),("On neuron", "Off neuron"))
# plt.ylabel("Neuron")
# plt.xlim(0, 1)
# plt.ylim(2.5, 0.5)

# # Plot the soma voltages of the neurons
# plt.subplot(222)
# #plt.plot(sim.trange(), sim.data[voltage][:, 0], "r")
# plt.plot(t, sim.data[voltage][:, 0] + 1, "r")
# plt.plot(t, sim.data[voltage][:, 1], "k")

# plt.xlim(0, 1)
plt.show()