MIT Technology Review February 19, 2018
Some critical machine-learning mechanisms have no analogue in the natural world, where learning seems to occur in a different way. Researchers at the University of Washington have created an artificial neural network that mimics the structure and behavior of the olfactory learning system in Manduca sexta moths. Their model can robustly learn new odors, and their simulations of integrate-and-fire neurons match the statistical features of in vivo firing rate data. This work that could have significant implications for the design of synthetic neural networks that need to learn quickly…read more. Open Access TECHNICAL ARTICLE
