Decoded vs Direct Connections

[xchoo]

@iampaulq

Yes, I want to inject a constant bias. I think it should be possible by just setting the bias, right? I have been using the bias and gain, instead of max_rates and intercept. I wanted each neuron to receive a bias current proportional to their respective pixel input.

Yup! You can set a bias current by modifying the bias and gain values. Using nengo.Node is an alternative way (in my opinion, a more flexible way) to introduce bias currents to neurons:

ens = nengo.Ensemble(4, d)
bias_node = nengo.Node(1)

bias_transform = [[b0], [b1], [b2], [b3]]  # bN is the bias values for the Nth neuron

nengo.Connection(bias_node, ens.neurons, transform=bias_transform)

@atait

Even if one is not using the NEF encoders, they are still being calculated at run time.
I wonder if there’s a way to avoid calculating the NEF encoders for an Ensemble that is just using “direct” connections. Is there some kind of raw Ensemble object that can just do the neuron nonlinearities, or is there some way to set encoders to avoid the operation?

Yes. You are correct. Unfortunately, in the current implementation of nengo.Ensemble in Nengo core, the encoder computation is done regardless of whether or not they are in use. We are aware of this, and are planning to change the Nengo API to facilitate this kind of network configuration in the future. These changes are planned to be incorporated into the Nengo 4.0 release.

I should note that this feature is already available in the current NengoDL release. If you are using NengoDL, it automatically removes any operators that do not add any effective computation to the operator graph.