The authors present a formalism for associative memory and pattern recognition performed by the time evolution of a dynamical system. The patterns are treated as multicomponent vectors, as well as continuous functions in space and time. Equations of motion are derived from a nonlinear potential and transformed to a low-dimensional subspace, where an appropriate form for neural nets is given. The example of rotated patterns shows how the formalism works in that case.
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