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Causal localization of neural function: the Shapley value method

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Causal localization of neural function: the Shapley value method. / Keinan, Alon; Hilgetag, Claus C.; Meilijson, Isaac; Ruppin, Eytan.

In: NEUROCOMPUTING, Vol. 58-60, 01.06.2004, p. 215-222.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Keinan, A, Hilgetag, CC, Meilijson, I & Ruppin, E 2004, 'Causal localization of neural function: the Shapley value method', NEUROCOMPUTING, vol. 58-60, pp. 215-222. https://doi.org/10.1016/j.neucom.2004.01.046

APA

Keinan, A., Hilgetag, C. C., Meilijson, I., & Ruppin, E. (2004). Causal localization of neural function: the Shapley value method. NEUROCOMPUTING, 58-60, 215-222. https://doi.org/10.1016/j.neucom.2004.01.046

Vancouver

Keinan A, Hilgetag CC, Meilijson I, Ruppin E. Causal localization of neural function: the Shapley value method. NEUROCOMPUTING. 2004 Jun 1;58-60:215-222. https://doi.org/10.1016/j.neucom.2004.01.046

Bibtex

@article{2670252b36f340d69401cc217b536404,
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”.",
author = "Alon Keinan and Hilgetag, {Claus C.} and Isaac Meilijson and Eytan Ruppin",
year = "2004",
month = jun,
day = "1",
doi = "10.1016/j.neucom.2004.01.046",
language = "English",
volume = "58-60",
pages = "215--222",
journal = "NEUROCOMPUTING",
issn = "0925-2312",
publisher = "ELSEVIER SCIENCE BV",

}

RIS

TY - JOUR

T1 - Causal localization of neural function: the Shapley value method

AU - Keinan, Alon

AU - Hilgetag, Claus C.

AU - Meilijson, Isaac

AU - Ruppin, Eytan

PY - 2004/6/1

Y1 - 2004/6/1

N2 - 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”.

AB - 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”.

U2 - 10.1016/j.neucom.2004.01.046

DO - 10.1016/j.neucom.2004.01.046

M3 - SCORING: Journal article

VL - 58-60

SP - 215

EP - 222

JO - NEUROCOMPUTING

JF - NEUROCOMPUTING

SN - 0925-2312

ER -