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.
import nengo import nengo_dl import numpy as np with 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
- faster simulation speed, on both CPU and GPU
- inserting networks defined using TensorFlow (such as convolutional neural networks) directly into a Nengo model
Check out the documentation for