> > I have attached an earlier 2006 paper with *_pictures_* of the learned > > > > transfer functions, which look a LOT like what is seen in a cat's an > > > > money's visual processing.> > ... which is so low-level that it counts as > > peripheral wiring. True. Still, it is kind of cool stuff for folks interested in how neural systems might self-organize from sensory data. The visual world has edges and borders at various scales and degrees of sharpness and it is interesting to see how that can be learned. Unfortunately, although the linearity assumptions of PCA might just barely allow this sort of "proto-V1" as in the paper, it doesn't seem likely to extend further up in a feature abstraction hierarchy where more complex relationships would seem to require nonlinearities. Assuming the author's analysis is correct, the observation that the discovered eigenvectors form groups that can express rotations of edge (etc) filters at various frequencies is kind of nifty, even if it turns out not to be biologically plausible. I don't see any broad generalityfor AGI beyond very low-level sensory processing given the limits of PCA and the sheer volume of training data required to sort out the principal components of high-dimensional inputs. For a much more detailed, capable, and perhaps more neurally plausible model of similar stuff, the work of Risto Miikkulainen's group is a lot of fun.
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