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Updated: Jun 19, 2026

Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Hebbian crosstalk prevents nonlinear unsupervised learning
Kingsley J A Cox1, Paul R Adams
1Department of Neurobiology, State University of New York Stony Brook Stony Brook, NY 11794, USA. kcox@notes.sunysb.edu
Abstract:
Learning is thought to occur by localized, activity-induced changes in the strength of synaptic connections between neurons. Recent work has shown that induction of change at one connection can affect changes at others ("crosstalk"). We studied the role of such crosstalk in nonlinear Hebbian learning using a neural network implementation of independent components analysis. We find that there is a sudden qualitative change in the performance of the network at a threshold crosstalk level, and discuss the implications of this for nonlinear learning from higher-order correlations in the neocortex.
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