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Ising model for inferring network structure from spike data
Stockholm University, Nordic Institute for Theoretical Physics (Nordita).
Stockholm University, Nordic Institute for Theoretical Physics (Nordita).
Stockholm University, Faculty of Science, Department of Mathematics.
2013 (English)In: Principle of Neural Coding / [ed] Rodrigo Quian Quiroga, Stefano Panzeri, Boca/Raton: CRC Press, 2013, 527-546 p.Chapter in book (Refereed)
Abstract [en]

Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.

Place, publisher, year, edition, pages
Boca/Raton: CRC Press, 2013. 527-546 p.
National Category
Biophysics
Identifiers
URN: urn:nbn:se:su:diva-74144DOI: 10.1201/b14756-31ISBN: 978-1-4398-5330-6 (print)ISBN: 978-1-4398-5331-3 (print)OAI: oai:DiVA.org:su-74144DiVA: diva2:506871
Available from: 2013-01-22 Created: 2012-03-01 Last updated: 2015-06-11Bibliographically approved

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Ising model for inferring network structure from spike data(578 kB)54 downloads
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Publisher's full textarxiv.org/abs/1106.1752

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Tyrcha, Joanna
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CiteExportLink to record
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Citation style
  • apa
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Output format
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