“Computation in the Human Cerebral Cortex Uses Less Than 0.2 Watts yet This Great Expense Is Optimal When considering Communication Costs”, 2020-04-25 (; similar):
[Later: 2021] Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical.
After establishing an energy-efficient viewpoint, we define computation and construct an energy-constrained, computational function that can be optimized.
This function implies a specific distinction between ATP-consuming processes, especially computation per se vs action potentials and other costs of communication. As a result, the partitioning of ATP-consumption here differs from earlier work. A bits/J optimization of computation requires an energy audit of the human brain. Instead of using the oft-quoted 20 watts of glucose available to the brain1, 2, the partitioning and audit reveals that cortical computation consumes 0.2 watts of ATP while long-distance communication costs are over 20× greater. The bits/joule computational optimization implies a transient information rate of more than 7 bits/sec/neuron.
Significance Statement: Engineers hold up the human brain as a low energy form of computation. However from the simplest physical viewpoint, a neuron’s computation cost is remarkably larger than the best possible bits/joule—off by a factor of 108.
Here we explicate, in the context of energy consumption, a definition of neural computation that is optimal given explicit constraints. The plausibility of this definition as Nature’s perspective is supported by an energy-audit of the human brain.
The audit itself requires certain novel perspectives and calculations revealing that communication costs are 20× computational costs.