Plasticity Kernels and Temporal Statistics
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 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.
Cite
Text
Dayan et al. "Plasticity Kernels and Temporal Statistics." Neural Information Processing Systems, 2003.Markdown
[Dayan et al. "Plasticity Kernels and Temporal Statistics." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/dayan2003neurips-plasticity/)BibTeX
@inproceedings{dayan2003neurips-plasticity,
title = {{Plasticity Kernels and Temporal Statistics}},
author = {Dayan, Peter and Häusser, Michael and London, Michael},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {1303-1310},
url = {https://mlanthology.org/neurips/2003/dayan2003neurips-plasticity/}
}