Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Neural system prediction and identification challenge
Show others and affiliations
2013 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 7, no DEC, 43- p.Article in journal (Refereed) Published
Abstract [en]

Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

Place, publisher, year, edition, pages
2013. Vol. 7, no DEC, 43- p.
Keyword [en]
network function, spiking neural network, nuSPIC, NEST, network simulation
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-154856DOI: 10.3389/fninf.2013.00043ISI: 000209207300040PubMedID: 24399966Scopus ID: 2-s2.0-84891521895OAI: oai:DiVA.org:kth-154856DiVA: diva2:758923
Note

QC 20150623

Available from: 2014-10-28 Created: 2014-10-28 Last updated: 2017-12-05Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMedScopus

Authority records BETA

Kumar, Arvind

Search in DiVA

By author/editor
Aertsen, AdKumar, Arvind
By organisation
Computational Biology, CB
In the same journal
Frontiers in Neuroinformatics
Neurosciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 81 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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