The Thermodynamic Analysis of Neural Computation

The brain displays a low-frequency ground energy confirmation, called the resting state, which is characterized by an energy/ information balance via self-regulatory mechanisms. Despite the high-frequency evoked activity, e.g., the detail-oriented sensory processing of environmental data and the accumulation of information, nevertheless the brain’s automatic regulation is always able to recover the resting state. Indeed, we show that the two energetic processes, activation that decreases temporal dimensionality via transient bifurcations and the ensuing brain’s response, lead to complementary and symmetric procedures that satisfy the Landauer’s principle. Landauer’s principle, which states that information era- sure requires energy predicts heat accumulation in the system, this means that information accumulation is correlated with increases in temperature and lead to 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 energy in synaptic connections. In deep learning, representation of information might occur via the same mechanism