Transform in Inhibitory Gating Example


#1

Hello,

I’ve been going through the Nengo examples, and I’m at a bit of a loss with the transform parameter passed to Connection in the Inhibitory Gating example. The section of code where it comes up is:

with model:
nengo.Connection(sin, A)
nengo.Connection(sin, B)
nengo.Connection(inhib, A.neurons, transform=[[-2.5]] * n_neurons)
nengo.Connection(inhib, C)
nengo.Connection(C, B.neurons, transform=[[-2.5]] * n_neurons)

The explanation of “transform” in the User Guide describes it as “Linear transform mapping the pre output to the post input”, which is fair enough, although I would appreciate a more detailed description if someone would be so kind as to explain it further. I am, however, particularly confused as to WHY it is equal to [[-2.5]] * n_neurons. Does this come from some other model of inhibitory gating networks, something analogous to a weight matrix in neural networks, or is there some biological/biophysical reasoning for it?

Best wishes,
Craig