Note
This documentation is for a development version. Click here for the latest stable release (v0.5.0).
Networks¶

nengo_extras.networks.
MatrixMult
(n_neurons, shape_left, shape_right, net=None)[source]¶ Computes the matrix product A*B.
Both matrices need to be two dimensional.
See the Matrix Multiplication example for a description of the network internals.
 Parameters
 n_neuronsint
Number of neurons used per product of two scalars.
Note
If an odd number of neurons is given, one less neuron will be used per product to obtain an even number. This is due to the implementation the
Product
network. shape_lefttuple
Shape of the A input matrix.
 shape_righttuple
Shape of the B input matrix.
 netNetwork, optional (Default: None)
A network in which the network components will be built. This is typically used to provide a custom set of Nengo object defaults through modifying
net.config
.
 Returns
 netNetwork
The newly built matrix multiplication network, or the provided
net
.

nengo_extras.networks.
Product
(n_neurons, dimensions, input_magnitude=1, net=None)[source]¶ Computes the elementwise product of two equally sized vectors.
Utilities¶

nengo_extras.probe.
probe_all
(net, recursive=False, probe_options=None, **probe_args)[source]¶ Probes all objects in a network.
 Parameters
 netnengo.Network
 recursivebool, optional (Default: False)
Probe subnetworks recursively.
 probe_options: dict, optional (Default: None)
A dict of the form {nengo_object_class: [attributes_to_probe]}. If None, every probeable attribute of every object will be probed.
 Returns
 A dictionary that maps objects and their attributes to their probes.
Examples
Probe the decoded output and spikes in all ensembles in a network and its subnetworks:
with nengo.Network() as model: ens1 = nengo.Ensemble(n_neurons=1, dimensions=1) node1 = nengo.Node(output=[0]) conn = nengo.Connection(node1, ens1) subnet = nengo.Network(label='subnet') with subnet: ens2 = nengo.Ensemble(n_neurons=1, dimensions=1) node2 = nengo.Node(output=[0]) probe_options = {nengo.Ensemble: ['decoded_output', 'spikes']} probes = probe_all(model, recursive=True, probe_options=probe_options)