Hebbian crosstalk prevents nonlinear unsupervised learning - Cox, Kingsley J.; Adams, Paul (Frontiers in Computational Neuroscience, 2009)
Learning is thought to occur by localized, activity-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 threshold crosstalk level and discuss the implications of this for nonlinear learning from higher-order correlations in the neocortex.