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Causal pattern recovery from neural spike train data using the Snap Shot Score

Lookup NU author(s): Dr Tom SmuldersORCiD

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Abstract

We present a new approach to learning directed in flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

Publication metadata

Author(s): Echtermeyer C, Smulders TV, Smith VA

Publication type: Article

Publication status: Published

Journal: Journal of Computational Neuroscience

Year: 2010

Volume: 29

Issue: 1-2

Pages: 231-252

Print publication date: 01/08/2010

Date deposited: 29/09/2010

ISSN (print): 0929-5313

ISSN (electronic): 1573-6873

Publisher: Springer

URL: http://dx.doi.org/10.1007/s10827-009-0174-2

DOI: 10.1007/s10827-009-0174-2

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