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Neural Nets, Connectionism, Perceptrons, etc.

03 Jun 2016 15:44

I'm mostly interested in them as a means of machine learning or statistical inference. I am particularly interested in their role as models of dynamical systems (via recurrent nets, generally), and as models of transduction.

I need to understand better how the analogy to spin glasses works, but then, I need to understand spin glasses better too.

The arguments that connectionist models are superior, for purposes of cognitive science, to more "symbolic" ones I find unconvincing. (Saying that they're more biologically realistic is like saying that cars are better models of animal locomotion than bicycles, because cars have four appendages in contact with the ground and not two.) This is not to say, of course, that some connectionist models of cognition aren't interesting, insightful and valid; but the same is true of many symbolic models, and there seems no compelling reason for abandoning the latter in favor of the former. (For more on this point, see Marcus, and my forthcoming review of his book.) --- Of course a cognitive model which cannot be implemented in real brains must be rejected; connecting neurobiology to cognition can hardly be too ardently desired. The point is that the elements in connectionist models called "neurons" bear only the sketchiest resemblance to the real thing, and neural nets are no more than caricatures of real neuronal circuits. Sometimes sketchy resemblances and caricatures are enough to help us learn, which is why Hebb, McCulloch and Neural Computation are important for both connectionism and neurobiology.


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