[nengo-user] Thalamus Template

[travis dewolf]

Hi Brian,

xBiased is going to be the output that you want to hook up to.
The idea with the xBiased output is that the thalamus model flips the
inhibitory signal from the basal ganglia into an excitatory signal.

If there's no input from the basal ganglia, xBiased will output all ones,
activating everything it's connected to.
Say you're connected to 4 possible actions, then the thalamus output by
default is an excitatory [1, 1, 1, 1], which would cause all your actions
to be activated.

The role of the basal ganglia is to* inhibit the excitatory output* from
the thalamus for all actions but those that win the winner-take-all
competition inside the basal ganglia. So whichever dimension wins the
competition in the basal ganglia circuit will have an output from the GPe
of 0, and all of the losers will have some positive value. This output
signal (e.g. [0, 1, 1, 1] where the first element is the winner and the
rest lose) gets sent to the thalamus, and says 'apply no inhibition to the
first dimension, and inhibit the second, third, and fourth dimensions'.

The way that we've implemented this in the circuit is by having an origin
in the thalamus that by default outputs 1, and subtracts out the input from
the basal ganglia. So xBiased returns [1, 1, 1, 1] - [x[0], x[1], x[2],
x[3]], where x is the signal from the basal ganglia.

Does that help clarify?


On Thu, Jun 19, 2014 at 10:28 AM, Brian Krainer <bkrainer731 at gmail.com>
wrote:

> Hello,
>
> I have a quick question about the Thalamus template. When I add a Thalamus
> to my network it has two origins, X and xBiased. I looked at the graphs of
> these in the interactive plots, as well as the code where they are defined,
> but I'm still a little bit confused by them. Could you possibly elaborate
> on how they work and why?
>
> Thanks,
>
> Brian
>
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