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要旨:
Advances in Machine Learning (ML) models have surpassed our ability to understand the inner “thinking process” that led to their results, leaving us with the same interrogations we ask to understand their biological counterpart, the brain. Here, the geometry of collective neural representations seems to be a crucial characteristic of information processing in both artificial and natural neural networks (NN). However, the dynamical aspect of these neural maps and the cognitive processes that operate on them are still largely unexplored.
