A neuron model with fluid properties for solving labyrinthian puzzle
type: Item
instance of: scholarly article
title: A neuron model with fluid properties for solving labyrinthian puzzle
zbMATH Open document ID: 0722.68092
DOI: 10.1007/BF00203631
author: D. Kharzeev
published in: Biological Cybernetics
publication date: 1990-01-01T00:00:00Z
review text: The continuous Hopfield model with constraint on the weights \(T_{ii}\neq 0\) replaced by \(T_{ii}=-\Sigma T_{ij}\) for all \(j\neq i\), allows to interpret the net as a model of flowing of a liquid affected only by gravity. The potentials of neurons then ``flow'' from higher to lower levels converging to a unique stable state. An example shows how this net can be used to solve an AI problem: in a labyrinth represented by the net with connections to neighbors either blocked or free, the stable state indicates the path through the labrinth by following decreasing potentials of representing neurons.
reviewed by: J.Horejs
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 76A02
zbMATH DE Number: 4191150
zbMATH Keywords: Hopfield model
MaRDI profile type: Publication
cites work: Neurons with graded response have collective computational properties like those of two-state neurons.
cites work: ``Neural'' computation of decisions in optimization problems
full work available at URL: https://doi.org/10.1007/bf00203631
OpenAlex ID: W1999003710
description: scientific article; zbMATH DE number 4191150