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Frontiers in Computational Neuroscience www.frontiersin.org August 2009 | Volume 3 | Article 11 | 1 COMPUTATIONAL NEUROSCIENCE
 

Summary: Frontiers in Computational Neuroscience www.frontiersin.org August 2009 | Volume 3 | Article 11 | 1
COMPUTATIONAL NEUROSCIENCE
ORIGINAL RESEARCH ARTICLE
published: xx August 2009
doi: 10.3389/neuro.10.011.2009
et al., 2009). We found that useful learning is always still possible
provided that Hebbian adjustments retain some connection spe-
cificity, though it is degraded. However, there has been increasing
realization that at least in the neocortex unsupervised learning
must be sensitive to higher-than-pairwise correlations, which
requires that the learning rule at individual connections be non-
linear. Since the number of possible higher-order correlations is,
for high-dimensional input patterns, essentially unlimited, useful
learningmightrequirethattheconnection-levelnonlinearlearning
rule be extremely accurate.
To test this idea, we studied the effect of introducing Hebbian
crosstalk in perhaps the simplest neural network model of non-
linear learning, independent components analysis (ICA) (Bell and
Sejnowski, 1995; Hoyer and Hyvärinen, 2000; Hyvärinen et al.,
2001; Nadal and Parga, 1994). In this model, it is assumed that the

  

Source: Adams, Paul R. - Department of Neurobiology and Behavior, SUNY at Stony Brook

 

Collections: Biology and Medicine