[nengo-user] using nengo for linear or nonlinear regression

[Chris Eliasmith]

Hi Mansoureh,

Yes, there are several ways to solve this problem.  If you have a 
favourite, you can use Nengo to implement it in various ways (e.g. 
with/without spiking, with/without neural dynamics, etc.).

As for an NEF specific solution, the circuit described in this MacNeil 
paper is an example:

http://compneuro.uwaterloo.ca/publications/macneil2011.html

In fact, those methods are more powerful than explored there, as the 
feedback error function can be generated by a nonlinear or linear 
target, as described more here:

http://compneuro.uwaterloo.ca/publications/bekolay2013.html

Just as a general pointer, if you search for 'learning' here: 
http://compneuro.uwaterloo.ca/publications.html  you'll find several 
items of potential interest.

Finally, chapter 9 of the Neural Engineering book (Eliasmith & Anderson, 
2003) has a parameter estimation example using function representation 
that could be what you're after (e.g. if the noise term in your equation 
changes).

Best, .c

Mansoureh Fahimi wrote:
> Hello nengo community,
>
> I am trying to implement nengo to perform a simple linear ar 
> regression of order 1. for this I have an input time series, and I 
> want to find the parameters c and phi for the following equation: 
> x(t)=c+phi*x(t-1)+e where e is gaussian noise. There are numerous ways 
> to solve this problem, but I am curious as to how the Neural 
> Engineering Framework can be applied to solve this equation for a 
> given time series.
>
> As far as I have understood, this is implemented as a simple 
> integrator plus an unknown constant input (c)  in nengo where the 
> transform weight is also unknown (phi). But I can't figure out how it 
> goes from hereon. is there a paper with a similar problem you can 
> refer me to?
>
> I appreciate your help in advance.
> Sincerely,
>
> Mansoureh Fahimi
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