Abstract

This paper presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe the Functional Contribution Analysis (FCA) which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multi-lesions is maximized. The approach is thoroughly examined on neurocontroller networks of evolved autonomous agents. The FCA portrays a stable set of neuronal contributions and accurate multi-lesion predictions, which are significantly better than those obtained based on the classical single lesion approach. It is also utilized for a detailed synaptic analysis of the neurocontroller connectivity network, delineating its main functional backbone. The FCA provides a...

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