Causal Localization of Neural Function : The Shapley Value Method

Identifying the functional roles of elements of a neural network is one of the first challenges in understanding neural information processing. Aiming at this goal, lesion studies have been used in neuroscience, most of which employing single lesions and hence, limited in their ability to reveal the significance of interacting elements. This paper presents the , an axiomatic, scalable and rigorous method, addressing the challenge of calculating the contributions of network elements from a multi-lesion data set. 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”