[nengo-user] Getting/interpreting neural weights

[Trevor Bekolay]

Hey Larry! Good to hear from you again :)

Could you post your question on the Nengo forum at https://forum.nengo.ai/
? We're in the process of phasing out this mailing list so that this kind
of info is easy to google.

- Trevor

On Sat, Jul 29, 2017 at 9:55 AM, Laurence Michaels <lmichaels at mail.usf.edu>
wrote:

> Hi All,
>
> This is Larry. I did the coin-flipping task at this summer's brain camp
> (which I loved). I'm still working on my project and have confirmed that my
> model consistently underestimates Alternations at a Probability of
> Alternation around 0.6. (This is the same area where people have a bias and
> will underestimate it as well.)
>
> Now, I want to figure out why. My current theory is that the learning
> process is not stable/does not converge under this substantial uncertainty.
> To research this, I've created a probe to look at the neural weights. I
> have a two dimensional input ensemble (200 neurons) connected via PES
> learning to a one dimensional prediction (100 neurons). When I use a probe
> on the connection to get the weights, it is 3 dimensions:
>
> - dimension 0 is the time during the learning. The range for this index
> varies depending upon the 'sample_every' parameter.
>
> - dimension 1 only has a range of 1 (value = 0)
>
> - dimension 2 has a range of 200 (the size of my input ensemble)
>
> I expected the weights to have 3 dimensions (time, 200 for the input
> ensemble, 100 for the output ensemble). I seem to be mis-understanding
> something. What are the definitions of the dimensions? Any thoughts or
> advice?
>
> Best,
>
> Larry
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