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- W4386435113 abstract "Human and animal experiments have shown that acquiring and storing information can require substantial amounts of metabolic energy. However, computational models of neural plasticity only seldom take this cost into account, and might thereby miss an important constraint on biological learning. This review explores various ways to reduce energy requirements for learning in neural networks. By comparing the resulting learning rules to cognitive and neurophysiological observations, we discuss how energy efficiency might have shaped biological learning." @default.
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- W4386435113 date "2023-12-01" @default.
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- W4386435113 title "Energetically efficient learning in neuronal networks" @default.
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- W4386435113 doi "https://doi.org/10.1016/j.conb.2023.102779" @default.
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