The brain's unique take on algorithms

Abstract

Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the development of neural computing technologies such as neuromorphic hardware and artificial intelligence. Furthermore, we discuss how this balanced perspective may be necessary to incorporate the neurobiological details that are critical for describing the neural computational disruptions within mental health and neurological disorders.

Fig. 1. Different disciplines have different perspectives of the brain.

Like the parable of the blind men and the elephant, different research communities (illustrated as neurons) often see only what they expect to see in the brain. A computer scientist’s perspective (green neuron) may be biased towards neural networks with established utility, a physicist (red neuron) may seek the energy landscapes that have proven invaluable for other questions, and a neuroscientist (blue neuron) may aim to describe the incredibly complex biology of neural circuits. The pursuit of a common theoretical framework that bridges top-down and bottom-up computational perspectives while allowing the realities of biology to be incorporated will be critical in furthering our understanding of the brain.