Analysis of competition-based spreading activation in connectionist models

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Abstract

In this paper we analyse a connectionist model of information processing in which the spread of activity in the network is controlled by the nodes actively competing for available activation. This model meets the needs of various artificial-intelligence tasks and has demonstrated several useful properties, including circumscribed spread of activation, stability of network activation following termination of external influences, and context-sensitive “winner-take-all” phenomena without explicit inhibitory links between nodes representing mutually exclusive concepts. We examine three instances of the competition-based connectionist model. For each instance, we show that the differential equations modelling the changes in the activation level of each node has a solution, and we prove that given any initial activity values of the nodes, certain equilibrium activation levels are reached. In particular, we demonstrate that lateral inhibition, i.e. mutually exclusive activity for nodes in the same layer, is possible without explicitly including links between nodes in the same layer. We believe that our results for these instances of the model give important insights into the behaviour observed in the general model.

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