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Title:Learning Synaptic Clusters for Nonlinear Dendritic Processing
Authors: Michael W. Spratling ; Gillian Hayes
Date:Feb 2000
Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear node which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses.
Michael Spratling and Gillian Hayes
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Bibtex format
author = { Michael W. Spratling and Gillian Hayes },
title = {Learning Synaptic Clusters for Nonlinear Dendritic Processing},
year = 2000,
month = {Feb},

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