  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.
