Deep Learning versus Biological Neurons: floating-point numbers, spikes, and neurotransmitters – from Matthew Roos

Original post from https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9

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 blog 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 the movement toward more realistic models of biological neurons might advance AI as we currently know it.

The details can be found from the original https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9