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  • Poster presentation
  • Open Access

Functional structure from dynamic clustering of spike train data

  • 1Email author,
  • 2,
  • 3,
  • 3 and
  • 1, 4
BMC Neuroscience20089 (Suppl 1) :P18

https://doi.org/10.1186/1471-2202-9-S1-P18

  • Published:

Keywords

  • Experimental Data
  • Animal Model
  • Topological Structure
  • Neuronal Network
  • Spike Train

We propose a new algorithm for detecting functional structure in neuronal networks based solely upon the information derived from the spike timings of the neurons. Unlike traditional algorithms that depend on knowledge of the topological structure of the network to parse the network into communities, we dynamically cluster the neurons to build communities with similar functional interactions. We define means to derive optimal clustering parameters and investigate what conditions have to be fulfilled to obtain reasonable predictions of functional structures. The success of the algorithm is verified using simulated spike train data, and we provide examples of the application of our method to experimental data where it detects known changes in neural states.

Declarations

Acknowledgements

This work was supported through an NSF Graduate Research Fellowship, NIH Grant EB003583, the Whitehall Foundation, and National Institute on Drug Abuse RO1 DA14318.

Authors’ Affiliations

(1)
Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
(2)
Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
(3)
Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
(4)
Biophysics Research Division, University of Michigan, Ann Arbor, MI 48109, USA

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