[nengo-user] Calculating dot product using spiking neurons

[Claus Agerskov]

HiI have a model where an ensemble stores four different semantic pointer in the form of 4D vectors. To this ensemble is coupled a simplenode input that shows one of those four pointers every half a second. Both input and storage ensembles are outputting to a another ensemble that is 20 dimensional. It takes five 4D inputs, which represents every pattern stored (4) and the simplenode (1). The ensemble that takes the input then computes four dot products, comparing each pattern to the (simplenode) input. This in effect should give a signal of 1 every time a pattern is recognized. My problem is however that this system only works if the ensemble comparing the inputs is using direct mode. I assume this is because the NEF algorithm cannot compute the necessary weights. Is this because the full weight matrix between the inputs and the dot product ensemble is not square (currently a 4x5-matrix)? As I have looked through a lot of Eliasmith's and his colleague's papers and the How to Build a Brain book, I see that the Basal Ganglia model as well as the Clean-up memory model is performing the same function that I am interested in but simply by having the dot product ensemble have direct access to the full vocabulary which doesn't seem very biologically realistic. Am I required to using a direct mode ensemble or is there a better way of comparing two semantic pointer outputs using the dot product? Sorry for the very long question, hope you will forgive me!With best regardsClaus Agerskov 		 	   		  
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://artsservices.uwaterloo.ca/pipermail/nengo-user/attachments/20141121/60bd9c45/attachment-0002.html>