For a good introductory graphical description of this network, see P. DENNING (1992b, p.427 -8).
HOPFIELD introduces nonlinear dynamics in computational networks. In DENNING's words: "HOPFIELD showed that his network can memorize patterns by storing them directly into the weights; no training is needed. There is a feedback path from the output of each unit to the input of every other unit, and all of these paths are assigned individual weights… Ultimately the network settles into a condition of equilibrium… If an input pattern does not exactly match one of the memorized patterns, the network will reach equilibrium at the nearest memorized pattern. This behavior provides a means for restoring a pattern from a noisy or a partial version" (Ibid).
HOPFIELD's networks are somehow reminiscent of ASHBY's homeostat.
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Bertalanffy Center for the Study of Systems Science (2020). Title of the entry. In Charles François (Ed.), International Encyclopedia of Systems and Cybernetics (2). Retrieved from www.systemspedia.org/[full/url]
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