Plasticity Kernels and Temporal Statistics

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Peter Dayan, Michael Häusser, Michael London


Computational mysteries surround the kernels relating the magnitude and sign of changes in efficacy as a function of the time difference between pre- and post-synaptic activity at a synapse. One important idea34 is that kernels result from fil(cid:173) tering, ie an attempt by synapses to eliminate noise corrupting learning. This idea has hitherto been applied to trace learning rules; we apply it to experimentally-defined kernels, using it to reverse-engineer assumed signal statistics. We also extend it to consider the additional goal for filtering of weighting learning according to statistical surprise, as in the Z-score transform. This provides a fresh view of observed kernels and can lead to different, and more natural, signal statistics.