Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Perturbing low dimensional activity manifolds in spiking neuronal networks
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Dept.of Neuroscience,Karolinska Institutet, Sweden. (Computational Brain Science)
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).ORCID iD: 0000-0002-8044-9195
2019 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 5, article id e1007074Article in journal (Refereed) Published
Abstract [en]

Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. In particular, this connectivity pattern appears to be constraining learning so that only neural activity patterns falling within the intrinsic manifold can be learned and elicited. Here, we use three different models of spiking neural networks (echo-state networks, the Neural Engineering Framework and Efficient Coding) to demonstrate how the intrinsic manifold can be made a direct consequence of the circuit connectivity. Using this relationship between the circuit connectivity and the intrinsic manifold, we show that learning of patterns outside the intrinsic manifold corresponds to much larger changes in synaptic weights than learning of patterns within the intrinsic manifold. Assuming larger changes to synaptic weights requires extensive learning, this observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.

Place, publisher, year, edition, pages
Public Library of Science , 2019. Vol. 15, no 5, article id e1007074
Keywords [en]
Learning, neuron network model, brain, low-dimensional dynamics, brain dynamics
National Category
Neurosciences
Research subject
Physics, Biological and Biomedical Physics
Identifiers
URN: urn:nbn:se:kth:diva-259350DOI: 10.1371/journal.pcbi.1007074ISI: 000471040500068PubMedID: 31150376Scopus ID: 2-s2.0-85067954297OAI: oai:DiVA.org:kth-259350DiVA, id: diva2:1351048
Funder
Swedish Research Council
Note

QC 20190917

Available from: 2019-09-13 Created: 2019-09-13 Last updated: 2019-09-17Bibliographically approved

Open Access in DiVA

fulltext(2367 kB)4 downloads
File information
File name FULLTEXT01.pdfFile size 2367 kBChecksum SHA-512
a3e5beede7952c6824970ce8a948cc0302a2a08fff0497b33cb747cd0e3b6d8e5561d247a75413c1dcf7c2e8fd41a363abd8420d8bb6ce5e0d7822a96f136fc3
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Search in DiVA

By author/editor
Wärnberg, EmilKumar, Arvind
By organisation
Computational Science and Technology (CST)
In the same journal
PloS Computational Biology
Neurosciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 4 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 38 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf