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Neurocomputing
Volumes 58-60, June 2004, Pages 215-222
Computational Neuroscience: Trends in Research 2004
 
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doi:10.1016/j.neucom.2004.01.046    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2004 Elsevier B.V. All rights reserved.

Causal localization of neural function: the Shapley value method

Alon KeinanCorresponding Author Contact Information, E-mail The Corresponding Author, a, Claus C. HilgetagE-mail The Corresponding Author, c, Isaac MeilijsonE-mail The Corresponding Author, b and Eytan RuppinE-mail The Corresponding Author, a, d

a Schools of Computer Science, Tel-Aviv University, Ramat Aviv, Tel-Aviv 69978, Israel b School of Mathematical Sciences, Tel-Aviv University, Ramat Aviv, Tel-Aviv 69978, Israel c School of Engineering and Science, International University Bremen, Bremen, Germany d School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv 69978, Israel

Available online 2 March 2004.

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Abstract

Identifying the functional roles of elements of a neural network is one of the fundamental challenges in understanding neural information processing. Aiming at this goal, lesion studies have been used extensively in neuroscience. Most of these employ single lesions and hence, limited ability in revealing the significance of interacting elements. This paper presents the multi-perturbation Shapley value analysis (MSA), an axiomatic, scalable and rigorous method, addressing the challenge of determining the contributions of network elements from a data set of multi-lesions or other perturbations. The successful workings of the MSA are demonstrated on artificial and biological data. MSA is a novel method for causal function localization, with a wide range of potential applications for the analysis of reversible deactivation experiments and TMS-induced “virtual lesions”.

Author Keywords: Localization of function; Multi-lesions; Shapley value; Contributions analysis; Interactions; Multi-perturbations

Article Outline

1. Introduction: The multi-perturbation Shapley value analysis
2. A test case
3. Analysis of evolved autonomous agents
4. MSA of biological data: reversible deactivation experiments
5. Conclusions
Acknowledgements
References
Vitae




Neurocomputing
Volumes 58-60, June 2004, Pages 215-222
Computational Neuroscience: Trends in Research 2004
 
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