10/09/2024
Did you know that an artificial neural network is designed to mimic the brain?
Inspired by biological neurons in the brain, artificial neural networks are large collections of “neurons”, or nodes, connected by “synapses”, or weighted couplings, which are trained to perform certain tasks. An artificial neural network processes information using its entire network structure. The inspiration
initially came from the desire to understand how the brain works.
In the 1940s, researchers had started to reason around the mathematics that underlies the brain’s network of neurons and synapses. Another piece of the puzzle came from psychology, thanks to neuroscientist Donald Hebb’s hypothesis about how learning occurs because connections between neurons are reinforced when they work together.
Later, these ideas were followed by attempts to recreate how the brain’s network functions by building artificial neural networks as computer simulations. In these, the brain’s neurons are mimicked by nodes that are given different values, and the synapses are represented by connections between the nodes that can be made stronger or weaker. Hebb’s hypothesis is still used as one of the basic rules for updating artificial networks through a process called training.
At the end of the 1960s, some discouraging theoretical results caused many researchers to suspect that these neural networks would never be of any real use. However, interest in artificial neural networks was reawakened in the 1980s, when several important ideas made an impact, including work by this year’s laureates John Hopfield and Geoffrey Hinton.
Learn more about this year’s physics prize awarded for work on artificial neural networks: https://bit.ly/4gK57jl