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The brain carries out enormously diverse and complex information processing operations to deal with a constantly varying world on a power budget of about 12-20 W. We argue that this efficiency is achieved in part through the dedication of specialized circuit elements and architectures to specific computational tasks, in a hierarchy stretching from the scale of neurons to scale of the entire brain, in sharp contrast to the conventional von Neumann architectures. This paper suggests that the heterogeneous computational repertoires of the brain are architectural memories of efficient computational procedures that are learned via evolutionary selection.
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Vijay Balasubramanian (2015) studied this question.
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