Deep learning integration for Nengo

NengoDL is a simulator for Nengo models. That means it takes a Nengo network as input, and allows the user to simulate that network using some underlying computational framework (in this case,TensorFlow).

In practice, what that means is that the code for constructing a Nengo model is exactly the same as it would be for the standard Nengo simulator.  All that changes is that we use a different Simulator class to execute the model.

For example:

import nengoimport nengo_dlimport numpy as npwith nengo.Network() as net:
    inp = nengo.Node(output=np.sin)
    ens = nengo.Ensemble(50, 1, neuron_type=nengo.LIF())
    nengo.Connection(inp, ens, synapse=0.1)
    p = nengo.Probe(ens)with nengo_dl.Simulator(net) as sim: # this is the only line that changes
    sim.run(1.0)print(sim.data[p])

However, NengoDL is not simply a duplicate of the Nengo simulator.  It also adds a number of unique features, such as:

• optimizing the parameters of a model through deep learning training methods (using the Keras API)

• faster simulation speed, on both CPU and GPU

• inserting networks defined using TensorFlow (such as deep learning architectures) directly into a Nengo model

Documentation

Check out the documentation for

• Installation instructions

• Details on the unique features of NengoDL

• Tutorial for new users with a TensorFlow background

• Tutorial for new users with a Nengo background

• More in-depth examples

• API reference