Fast information processing with slow neurons

Bernese researchers have developed a theory that shows how the brain can efficiently learn extremely fast sequences of sensory stimuli. This can happen much quicker than previously thought if neurons (nerve cells) have a mechanism that allows them to "predict" the future. The Bernese work was selected for presentation from among nearly ten thousand submitted papers at the world's most important conference on artificial intelligence. This year's NeurIPS conference (Conference on Neural Information Processing Systems) - the most important forum on artificial intelligence for decades - has selected a paper by a team led by Dr. Mihai Petrovici from the University of Bern for oral presentation. This places the selected article among the top 1% of the nearly ten thousand research papers submitted this year. In their study, the Bernese researchers looked at how the deep, complex neuronal networks in the brain learn to recognize sensory stimuli. "Imagine a child sees a bicycle for the first time," explains Paul Haider, first author of the study. "The information flows from the retina in the eye through many neurons until it reaches a region in the brain where neurons specialize to capture this new concept of a bicycle. However, not only do these few high-level neurons have to learn to recognize bicycles as such, but all the neurons in between must also adapt to process the visual information as efficiently as possible." This problem is known in neuroscience as the "credit assignment problem": What contribution do individual neurons make to the function of the network as a whole?