Plasticity kernels and temporal statistics
Peter Dayan Michael Häusser
Mickey London
NIPS 2003
Abstract
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
idea (Wallis & Baddeley, 1997) is that kernels result from
filtering, 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.
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