Neurons as an Information-theoretic Engine
We show that dynamical gain modulation of neurons' stimulus response is described as an information-theoretic cycle that generates entropy associated with the stimulus-related activity from entropy produced by the modulation. To articulate this theory, we describe stimulus-evoked activity of a neural population based on the maximum entropy principle with constraints on two types of overlapping act
Chainerを使ã£ã¦ã‚³ãƒ³ãƒ”ュータã«ã‚¤ãƒ©ã‚¹ãƒˆã‚’æã‹ã›ã‚‹ - Qiita
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The top A.I. breakthroughs of 2015 « the Kurzweil Library + collections
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