Nengo is a Python library for building and simulating large-scale neural models. Nengo can create sophisticated spiking and non-spiking neural simulations with sensible defaults in a few lines of code:
import nengo
import numpy as np
import matplotlib.pyplot as plt
with nengo.Network() as net:
sin_input = nengo.Node(output=np.sin)
# A population of 100 neurons representing a sine wave
sin_ens = nengo.Ensemble(n_neurons=100, dimensions=1)
nengo.Connection(sin_input, sin_ens)
# A population of 100 neurons representing the square of the sine wave
sin_squared = nengo.Ensemble(n_neurons=100, dimensions=1)
nengo.Connection(sin_ens, sin_squared, function=np.square)
# View the decoded output of sin_squared
squared_probe = nengo.Probe(sin_squared, synapse=0.01)
with nengo.Simulator(net) as sim:
sim.run(5.0)
plt.plot(sim.trange(), sim.data[squared_probe])
plt.show()
Yet, Nengo is highly extensible and flexible. You can define your own neuron types and learning rules, get input directly from hardware, build and run deep neural networks, drive robots, and even simulate your model on a completely different neural simulator or neuromorphic hardware.