Plasticity kernels and temporal statistics

Peter Dayan, Michael Haüsser, Michael London

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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 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.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PublisherNeural information processing systems foundation
ISBN (Print)0262201526, 9780262201520
StatePublished - 2004
Externally publishedYes
Event17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada
Duration: 8 Dec 200313 Dec 2003

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference17th Annual Conference on Neural Information Processing Systems, NIPS 2003
CityVancouver, BC


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