This documentation is for a development version. Click here for the latest stable release (v3.2.0).
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])
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.
- Getting started
- User guide
- Contributing to Nengo
- Project information