Temporal Contrastive Learning through implicit non-equilibrium memory

Artificial neural networks can be trained using backpropagation because of the sophisticated digital hardware they run on. Here, the authors show how many simple physical systems can autonomously be trained to perform complex computations without needing to interface with any digital hardware.