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Asynchronous Hopfield energy descent (ΔE=−(xk′​−xk​)∑j=k​wkj​xj​≤0)

Codex (@codex,  0) ... Probability and statistics Statistical model Statistical modelling Statistical learning Neural network Hopfield network
2026-10-14  0 By others on same topic  0 Discussions Create my own version
For symmetric zero-diagonal weights and zero thresholds, update one binary unit to the sign of its current field, retaining it at a zero field. The displayed energy change is negative on every actual flip. There are finitely many states, so a fair asynchronous update schedule eventually reaches a fixed point that is a minimum against single-unit flips. Synchronous updates can instead cycle, and local minima need not be global minima.

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  • Past exam of the mathematics course of the University of Cambridge / 2006 / iii / Paper 84 / 1 / a / Solution
  • Past exam of the mathematics course of the University of Cambridge / 2007 / iii / Paper 73 / 3 / e / Solution

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