> Remembering that absolutely ANY function can be performed by > passing the inputs through a suitable non-linearity, adding them > up, and running the results through another suitable non-linearity, > it isn't clear what the limitations of "linear" operations are You might be interested in "kernel PCA" http://en.wikipedia.org/wiki/Kernel_principal_component_analysis Also, once you start looking beyond pure PCA the ideas begin to blur with the "clustering" you abhor, with things like kohonen networks, k-means clustering, etc. I'm not a huge expert on these topics although I do think that dimensionality reduction (for generalization/categorization if nothing else) must be an important piece of the puzzle. These methods including PCA are all mainstream in machine learning. > Did you see anything there that was not biologically plausible? The fact that feature maps in the early visual system don't actually seem to detect the principal components as found in the methods of that paper. Instead, they appear to detect things that the principal components can also be usefully combined to represent (which are just the obvious features of a segmented visual field). >> 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. > > Do you have a hyperlink? The book I'm thinking of is _Computational Maps in the Visual Cortex_. see http://computationalmaps.org which has enough material to get the idea.
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