Ranit Aharonov-Barki, Isaac Meilijson, Eytan Ruppin
We introduce a novel algorithm, termed PPA (Performance Prediction Algorithm), that quantitatively measures the contributions of elements of a neural system to the tasks it performs. The algorithm identifies the neurons or areas which participate in a cognitive or behavioral task, given data about performance decrease in a small set of lesions. It also allows the accurate prediction of performances due to multi-element lesions. The effectiveness of the new algorithm is demonstrated in two models of recurrent neural networks with complex interactions among the ele(cid:173) ments. The algorithm is scalable and applicable to the analysis of large neural networks. Given the recent advances in reversible inactivation techniques, it has the potential to significantly contribute to the under(cid:173) standing of the organization of biological nervous systems, and to shed light on the long-lasting debate about local versus distributed computa(cid:173) tion in the brain.