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dc.contributor.authorScholl, Carolin
dc.contributor.authorRule, M
dc.contributor.authorHennig, Matthias H
dc.date.accessioned2021-11-12T18:57:11Z
dc.date.available2021-11-12T18:57:11Z
dc.date.issued2021-10
dc.date.submitted2020-11-30
dc.identifier.issn1553-734X
dc.identifier.otherpcompbiol-d-20-02149
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330615
dc.descriptionFunder: Studienstiftung des Deutschen Volkes; funder-id: http://dx.doi.org/10.13039/501100004350
dc.descriptionFunder: Bundesministerium für Bildung und Forschung; funder-id: http://dx.doi.org/10.13039/501100002347
dc.descriptionFunder: Max-Planck-Gesellschaft; funder-id: http://dx.doi.org/10.13039/501100004189
dc.description.abstractDuring development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning. Why networks should initially be over-populated, and the processes that determine which synapses and neurons are ultimately pruned, remains unclear. We study the mechanisms and significance of neural pruning in model neural networks. In a deep Boltzmann machine model of sensory encoding, we find that (1) synaptic pruning is necessary to learn efficient network architectures that retain computationally-relevant connections, (2) pruning by synaptic weight alone does not optimize network size and (3) pruning based on a locally-available measure of importance based on Fisher information allows the network to identify structurally important vs. unimportant connections and neurons. This locally-available measure of importance has a biological interpretation in terms of the correlations between presynaptic and postsynaptic neurons, and implies an efficient activity-driven pruning rule. Overall, we show how local activity-dependent synaptic pruning can solve the global problem of optimizing a network architecture. We relate these findings to biology as follows: (I) Synaptic over-production is necessary for activity-dependent connectivity optimization. (II) In networks that have more neurons than needed, cells compete for activity, and only the most important and selective neurons are retained. (III) Cells may also be pruned due to a loss of synapses on their axons. This occurs when the information they convey is not relevant to the target population.
dc.languageen
dc.publisherPublic Library of Science (PLoS)
dc.subjectResearch Article
dc.subjectBiology and life sciences
dc.subjectMedicine and health sciences
dc.subjectComputer and information sciences
dc.subjectSocial sciences
dc.titleThe information theory of developmental pruning: Optimizing global network architectures using local synaptic rules.
dc.typeArticle
dc.date.updated2021-11-12T18:57:10Z
prism.issueIdentifier10
prism.publicationNamePLoS Comput Biol
prism.volume17
dc.identifier.doi10.17863/CAM.78059
dcterms.dateAccepted2021-09-17
rioxxterms.versionofrecord10.1371/journal.pcbi.1009458
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Richards, Blake A
dc.contributor.orcidScholl, Carolin [0000-0003-0021-9435]
dc.contributor.orcidRule, M [0000-0002-4196-774X]
dc.contributor.orcidHennig, Matthias H [0000-0001-7270-5817]
dc.identifier.eissn1553-7358
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L027208/1)
cam.issuedOnline2021-10-11


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