288 pp. per issue
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Neural Computation

November 2009, Vol. 21, No. 11, Pages 2991-3009.
(doi: 10.1162/neco.2009.04-06-184)
© 2009 Massachusetts Institute of Technology
On the Maximization of Information Flow Between Spiking Neurons
Article PDF (274.66 KB)

A feedforward spiking network represents a nonlinear transformation that maps a set of input spikes to a set of output spikes. This mapping transforms the joint probability distribution of incoming spikes into a joint distribution of output spikes. We present an algorithm for synaptic adaptation that aims to maximize the entropy of this output distribution, thereby creating a model for the joint distribution of the incoming point processes. The learning rule that is derived depends on the precise pre- and postsynaptic spike timings. When trained on correlated spike trains, the network learns to extract independent spike trains, thereby uncovering the underlying statistical structure and creating a more efficient representation of the incoming spike trains.