One of the curious features of the deep neural networks behind machine learning is that they are surprisingly different from the neural networks in biological systems. While there are similarities, some critical machine-learning mechanisms have no analogue in the natural world, where learning seems to occur in a different way.
These differences probably account for why machine-learning systems lag so far behind natural ones in some aspects of performance. Insects, for example, can recognize odors after just a handful of exposures. Machines, on the other hand, need huge training data sets to learn. Computer scientists hope that understanding more about natural forms of learning will help them close the gap.
Enter Charles Delahunt and colleagues at the University of Washington in Seattle, who have created an artificial neural network that mimics the structure and behavior of the olfactory learning system in Manduca sexta moths. They say their system provides some important insights into the way natural networks learn, with potential implications for machines. [Continue reading…]