The VLSI kernel of neural algorithms

A unified description of neural algorithms by means of general objective functions is shown to be the key to economic design of software and hardware. The compute-intensive algorithmic strings present in the dynamical equations corresponding to an objective function are to be executed by dedicated VLSI circuits. Cellular neural networks are recovered as a special case, and a corresponding general learning rule is derived.<<ETX>>

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