How can neural networks compute with few resources? A lesson from mice.

Despite the unsustainable growth in energy consumption by artificial intelligence models and the recognition of the major role played by metabolic constraints in brain evolution, the relationship between computation and energy remains insufficiently studied and understood. Recently, Padamsey et al. experimentally investigated this relationship in the context of visual information processing in food-deprived mice. Combining analysis of their activity data and modeling inspired by statistical physics, in particular some variants of the Hopfield model, I will propose some mechanism by which neural circuits can spare considerable energy with little impact on their performance.

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