A neuron model with fluid properties for solving labyrinthian puzzle
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.
scientific article; zbMATH DE number 4191150