# Ensemble arrayΒΆ

An ensemble array is a group of ensembles that each represent a part of the overall signal.

Ensemble arrays are similar to normal ensembles, but expose a slightly different interface. Additionally, in an ensemble array, the components of the overall signal are not related. As a result, network arrays cannot be used to compute nonlinear functions that mix the dimensions they represent.

In [1]:

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import nengo

In [2]:

model = nengo.Network(label='Ensemble Array')
with model:
# Make an input node
sin = nengo.Node(output=lambda t: [np.cos(t), np.sin(t)])

# Make ensembles to connect
A = nengo.networks.EnsembleArray(100, n_ensembles=2)
B = nengo.Ensemble(100, dimensions=2)
C = nengo.networks.EnsembleArray(100, n_ensembles=2)

# Connect the model elements, just feedforward
nengo.Connection(sin, A.input)
nengo.Connection(A.output, B)
nengo.Connection(B, C.input)

# Setup the probes for plotting
sin_probe = nengo.Probe(sin)
A_probe = nengo.Probe(A.output, synapse=0.02)
B_probe = nengo.Probe(B, synapse=0.02)
C_probe = nengo.Probe(C.output, synapse=0.02)

In [3]:

# Set up and run the simulator
with nengo.Simulator(model) as sim:
sim.run(10)

In [4]:

# Plot the results
plt.figure()
plt.plot(sim.trange(), sim.data[sin_probe])
plt.plot(sim.trange(), sim.data[A_probe])
plt.plot(sim.trange(), sim.data[B_probe])
plt.plot(sim.trange(), sim.data[C_probe]);


These plots demonstrate that the network array works very similarly to a standard N-dimensional population. However, this is not true when it comes to computing functions. Network arrays cannot be used to compute nonlinear functions that mix the dimensions they represent.