<jupyter_start><jupyter_text>Only Positive feedback connections: With the matrix [[1, 1], [1, 1]], the left and right signals both rise steeply, with the left slightly ahead of the right. They then reach a horizontal asymptote. Left signal encounters a discontinuity before its slope begins to decrease. The left approaches a value slightly above 1. The right approaches a value slightly below 1.![Alt text](positiveFunct.png)Feedback connections set to zero: With the matrix [[0, 0], [0, 0]], left spikes then returns to near zero. Right dips briefly then returns to near zero.![Alt text](negativeFunct.png)<jupyter_code># YOUR CODE HERE
# plot the raster plot of the spiking neurons, see #https://www.nengo.ai/nengo-extras/examples/plot_spikes.html
plt.figure()
plot_spikes(sim.trange(), sim.data[spikes_probe])
plt.title("Neuron Sike Raster")
plt.xlabel("Time(s)")
plt.ylabel("Neuron ID")<jupyter_output><empty_output><jupyter_text>Only Positive feedback connections: There seem to be more neurons that do not ever spike. Neurons which do spike spike more frequently.![Alt text](positiveSpike.png)Feedback connections set to zero: Similar to only positive. Seems to have less spikes overall.![Alt text](negativeSpike.png)<jupyter_code># YOUR CODE HERE
# plot the Phase plot for your neuron population, using the decoded_output of the neurons. This is a [n_time,2] vector
# Please look at #https://www.nengo.ai/nengo/examples/dynamics/oscillator.html
phaseData = sim.data[neuron_probe]
plt.figure()
plt.plot(phaseData[:, 0], phaseData[:, 1])
plt.title("Signal Intensity as a Coordinate of Left and Right Intensity")
plt.xlabel("Left Intensity")
plt.ylabel("Right Intensity")<jupyter_output><empty_output>