Randomly Connected Sigma-Pi Neurons Can Form . . .
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Randomly connected sigma-pi neurons can form associative memories
A set of sigma-pi units randomly connected to two input vectors forms a disorganized type of hetero-associative memory related to convolutionand matrix-based associative memories. Associations are represented as patterns of activity rather than connection strengths. Decoding the associations requires another network of sigma-pi units, with connectivity dependent on the encoding network. Learnin...
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A feedforward Sigma-Pi neural network with a single hidden layer of m neurons is given by mSigma(j=1) cjg (nPi(k=1) xk-thetak(j)/lambdak(j)) where cj, thetak(j), lambdak are elements of R. In this paper, we investigate the approximation of arbitrary functions f: Rn-->R by a Sigma-Pi neural network in the Lp norm. An Lp locally integrable function g(t) can approximate any given function, if and ...
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