Hebbian crosstalk prevents nonlinear unsupervised learning

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...

Nonlinear Hebbian learning

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common pri...

An extended Hebbian model of unsupervised learning

The goal of learning is to produce such a configuration of the synaptic weights in a network that roughly encodes the properties of the object to be learned. Unsupervised learning relies on two factors: the input data and the network internal processing, with no external report on the state of synapses. Although it initially appeared that some systems have no internal objective monitor, a more ...

Signal Separation by Nonlinear Hebbian Learning

Hebbian crosstalk and input segregation.

Hebbian synapses respond to input/output correlations, and thus to input statistical structure. However, recent evidence suggests that strength adjustments are not completely connection-specific, and this "crosstalk" could distort, or even prevent, learning processes. Crosstalk would then be a form of adjustment mistake, analogous to mistakes in polynucleotide copying. The mutation rate must be...