ARTICLE Communicated by Gal Chechik A Spiking Neuron as Information Bottleneck

Neurons receive thousands of presynaptic input spike trains while emit-ting a single output spike train. This drastic dimensionality reduction suggests considering a neuron as a bottleneck for information transmis-sion. Extending recent results, we propose a simple learning rule for the weights of spiking neurons derived from the information bottleneck (IB) framework that minimizes the loss of relevant information trans-mitted in the output spike train. In the IB framework, relevance of in-formation is defined with respect to contextual information, the latter entering the proposed learning rule as a “third ” factor besides pre- and postsynaptic activities. This renders the theoretically motivated learning rule a plausible model for experimentally observed synaptic plasticity phenomena involving three factors. Furthermore, we show that the pro-posed IB learning rule allows spiking neurons to learn a predictive code, that is, to extract those parts of their input that are predictive for future input.