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