@article{0783f86efd214c37a2d78164f1a57037,
title = "Causal localization of neural function: The Shapley value method",
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{"}.",
keywords = "Contributions analysis, Interactions, Localization of function, Multi-lesions, Multi-perturbations, Shapley value",
author = "Alon Keinan and Hilgetag, {Claus C.} and Isaac Meilijson and Eytan Ruppin",
note = "Funding Information: We acknowledge the valuable contributions and suggestions made by Ranit Aharonov, Shay Cohen, Zohar Ganon, Ehud Lehrer and Keren Saggie and the technical help provided by Oran Singer. This research has been supported by the Adams Super Center for Brain Studies in Tel-Aviv University and by the Israel Science Foundation founded by the Israel Academy of Sciences and Humanities. ",
year = "2004",
month = jun,
doi = "10.1016/j.neucom.2004.01.046",
language = "אנגלית",
volume = "58-60",
pages = "215--222",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier B.V.",
}