Temporally dependent plasticity: An information theoretic account

The fundamental paradigm of Hebbian learning has recently re-ceived a novel interpretation with the discovery of synaptic plas-ticity that depends on the relative timing of pre and post synaptic spikes. [Markram et al. 1997; Zhang et al. 1998]. While this type of plasticity has already been studied in various computational frame-works, a generic computational motivation or its derivation from rst principles is still missing. In this paper we derive temporally dependent learning from the basic principle of mutual information maximization and study its relation to the experimentally observed plasticity. The comparison shows that not only is the biological learning rule similar in form to the analytically derived one, but it also increases mutual information to a near-optimal level. The analysis yields new insights into the temporal characteristics of the observed learning rule, and experimental predictions as to the time constants involved, depending on neuronal biophysical parameters.