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dc.contributor.author
Jegminat, Jannes
dc.contributor.author
Surace, Simone Carlo
dc.contributor.author
Pfister, Jean-Pascal
dc.date.accessioned
2022-06-08T09:07:23Z
dc.date.available
2022-03-05T14:59:07Z
dc.date.available
2022-06-07T12:29:20Z
dc.date.available
2022-06-08T09:07:23Z
dc.date.issued
2022-02-01
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.1009721
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/535509
dc.identifier.doi
10.3929/ethz-b-000535509
dc.description.abstract
Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network-the Synaptic Filter-and show its computational and biological relevance. For the computational relevance, we show that filtering improves the weight estimation performance compared to a gradient learning rule with optimal learning rate. The dynamics of the mean of the Synaptic Filter is consistent with spike-timing dependent plasticity (STDP) while the dynamics of the variance makes novel predictions regarding spike-timing dependent changes of EPSP variability. Moreover, the Synaptic Filter explains experimentally observed negative correlations between homo- and heterosynaptic plasticity.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
PLOS
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Learning as filtering: Implications for spike-based plasticity
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2022-02-23
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
18
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e1009721
en_US
ethz.size
23 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
San Francisco, CA
ethz.publication.status
published
en_US
ethz.date.deposited
2022-03-05T14:59:12Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-06-07T12:29:28Z
ethz.rosetta.lastUpdated
2024-02-02T17:23:50Z
ethz.rosetta.versionExported
true
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