Learning like machines

Neurobiologists at the Friedrich Miescher Institute for Biomedical Research (FMI) have shown that neurons critical for learning can be divided into two subpopulations with different functions. Almost as if learning processes in the brain mimicked machine learning, one subpopulation is responsible for collecting a broad range of potentially relevant information, while the second subsequently helps to consolidate a successful strategy. This is the first evidence of how neuron networks are adapted to facilitate learning. Self-driving cars, smartphones that can answer our questions and computers that can defeat world chess champions - we are increasingly surrounded by intelligent machines capable of dealing with a wide variety of situations. When machines 'learn', they follow clearly defined strategies specified by programmers. At the start of the learning process, they try out many different paths and test various tactics. As soon as they are close to a solution, however, their approach changes and they focus on refining a procedure that has proved successful.