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Learning Synaptic Clusters for Nonlinear Dendritic Processing

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Abstract

Nonlinear dendritic processing appears to be a feature of biological neuronsand would also be of use in many applications of artificial neuralnetworks. This paper presents a model of an initially standard linearnode which uses unsupervised learning to find clusters of inputs withinwhich inactivity at one synapse can occlude the activity at the othersynapses.

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Authors and Affiliations

  1. Department of Artificial Intelligence, University of Edinburgh, 5 Forrest Hill, Edinburgh, EH1 2QL, U.K.

    Michael W. Spratling & Gillian M. Hayes

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  1. Michael W. Spratling
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  2. Gillian M. Hayes
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Spratling, M.W., Hayes, G.M. Learning Synaptic Clusters for Nonlinear Dendritic Processing. Neural Processing Letters 11, 17–27 (2000). https://doi.org/10.1023/A:1009634821039

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