Deep Learning versus Biological Neurons: floating-point numbers, spikes, and neurotransmitters

In recent years, "deep learning" AI models have often been touted as "working like the brain," in that they are composed of artificial neurons mimicking those of biological brains. From the perspective of a neuroscientist, however, the differences between deep learning neurons and biological neurons are numerous and distinct. In this post we'll start by describing a few key characteristics of biological neurons, and how they are simplified to obtain deep learning neurons. We'll then speculate on how these differences impose limits on deep learning networks, and how movement toward more realistic models of biological neurons might advance AI as we currently know it. Typical biological neurons are individual cells, each composed of the main body of the cell along with many tendrils that extend from that body. The body, or soma, houses the machinery for maintaining basic cell functions and energy processing (e.g., the DNA-containing nucleus, and organelles for building proteins and processing sugar and oxygen).