Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energyefficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843? 1864 (1960)] and [J. Sawada, D. S. Modha, ?Synapse: Scalable energy-efficient neurosynaptic computing? in Application of Concurrency to System Design (ACSD) (2013), pp. 14?15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a 108-fold discrepancy between biological and lowest possible values of a neuron?s computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

Automated summary

Darwinian evolution tends to lead to energy-efficient outcomes, while energy limitations can impact computation processes, whether neural or digital. By focusing on neural computation from an energy-efficient perspective, the study explores the relationship between energy consumption and computational function in the brain, revealing new insights into energy partitioning and efficiency in cortical computation.