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arXiv · 0802.2967

Hebbian Crosstalk Prevents Nonlinear Unsupervised Learning

Kingsley J. A. Cox·Paul R. Adams
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Abstract

Learning is thought to occur by localized, experience-induced changes in the strength of synaptic connections between neurons. Recent work has shown that activity-dependent changes 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 (ICA). We find that there is a sudden qualitative change in the performance of the network at a critical crosstalk level and discuss the implications of this for nonlinear learning from higher-order correlations in the neocortex.

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Kingsley J. A. Cox, Paul R. Adams. 2008-02-21. Hebbian Crosstalk Prevents Nonlinear Unsupervised Learning. https://arxiv.org/abs/0802.2967

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