human
I am working on the file "Implementing a Braindrop-like architecture in Nengo.ipynb".
The first task is:
Computing with neurons
We now have spiking activity that reflects the changing input.  Now let's try to compute something using that input.  For this example, we'll just compute the product between the two values.
To do this, Nengo will compute decoders.  These are connection weights that will decode the spikes into some desired output value.  With the addition of the decoders, we now have a standard single-hidden-layer network where the first set of weights are the encoders and the second set of weights are the decoders.  Importantly, we do not have a neural non-linearity at the input or output -- the only neural non-linearities are in the hidden layer.  But we can use this to approximate any function, as single-hidden-layer networks are universal function approximators.
Nengo automatically computes these weights for you when you make a Connection out of an Ensemble.  In a more detailed simulator, we might constrain these weights by discretizing them or bounding them using a custom nengo.Solver class, but we don't do that here.  Instead, we use the default where nengo will automatically feed random inputs into the network, record the resulting firing rates, and then use least-squares minimization to compute the optimal output weights.
Can you write Python code for it?
assistant
#Here is Python code to compute something using that input.

import nengo
model = nengo.Network()
with model:
    stim = nengo.Node(nengo.processes.WhiteSignal(period=10, high=0.5), size_out=D)
    ens = nengo.Ensemble(n_neurons=N, dimensions=D, encoders=encoder.T,
                         gain=gain, bias=bias)
    nengo.Connection(stim, ens)
    
    output = nengo.Node(None, size_in=1)
    def my_function(x):
        return x[0]*x[1]
    nengo.Connection(ens, output, function=my_function, synapse=None)
    
    p_stim = nengo.Probe(stim)
    p_spikes = nengo.Probe(ens.neurons)
    p_output = nengo.Probe(output)
with nengo.Simulator(model, seed=0) as sim:
    sim.run(10)

    
plt.figure(figsize=(12,4))
plt.plot(sim.trange(), sim.data[p_output], label='raw decoded output')
filt = nengo.synapses.Lowpass(0.01)   # for smoothing the output
plt.plot(sim.trange(), filt.filt(sim.data[p_output]), label='smoothed output')
ideal = [my_function(x) for x in sim.data[p_stim]]
plt.plot(sim.trange(), ideal, ls='--', lw=2, label='ideal output', c='k')
plt.legend()
plt.show()