The brain displays a low-frequency ground energy conformation, called the resting state, which is characterized by energy/information balance via self-regulatory mechanisms. Despite the high-frequency evoked activity accumulates information from the detail-oriented sensory processing of environmental data, nevertheless the brain’s automatic regulation is always able to recover the resting state. Indeed, we show that the two energetic processes are complementary and symmetric: while activation decreases temporal dimensionality via transient bifurcations, the ensuing brain’s response leads to procedures that satisfy the Landauer’s principle. Landauer’s principle, which states that information erasure requires energy, predicts heat accumulation in the system: this means that information accumulation is correlated with increases in temperature and with actions that recover the resting state. We explain how brain synaptic networks frame a closed system, similar to the Carnot cycle, where the information/energy cycle accumulates as energy of synaptic connections, and then dissipates in the resting state. In deep learning, representation of information might occur via the same mechanism.
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[v1] 2017-10-17 10:05:28
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