Hebbian Crosstalk Prevents Nonlinear Unsupervised Learning

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.

• Publication year

  2008

• Venue

  Frontiers in Computational Neuroscience

• Publication date

  2008-02-21

• Fields of study

  Biology, Medicine, Computer Science, Psychology

• Identifiers
  DOI 10.3389/neuro.10.011.2009arXiv 0802.2967PMID 19826612PMCID 2759358
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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