Change search
ReferencesLink to record
Permanent link

Direct link
Neural system prediction and identification challenge
Show others and affiliations
2013 (English)In: Frontiers in Neuroinformatics, 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
URN: urn:nbn:se:kth:diva-154856DOI: 10.3389/fninf.2013.00043ISI: 000209207300040PubMedID: 24399966ScopusID: 2-s2.0-84891521895OAI: diva2:758923

QC 20150623

Available from: 2014-10-28 Created: 2014-10-28 Last updated: 2015-06-23Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMedScopus

Search in DiVA

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

Search outside of DiVA

GoogleGoogle Scholar
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

Altmetric score

Total: 13 hits
ReferencesLink to record
Permanent link

Direct link