Wu Tsai Neurosciences Institute Seminar Series Presents
Markus Meister, PhD
Anne P. and Benjamin F. Biaggini Professor of Biological Sciences, California Institute of Technology
Host: Shaul Druckmann
Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies of animal learning are based on abstract two-alternative-forced-choice (2AFC) tasks. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth. A mouse in the labyrinth makes about 2000 navigation decisions per hour. The animal quickly discovers the location of a reward in the maze and executes correct 10-bit choices after only 10 reward experiences – a learning rate 1,000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The animal's search algorithm does not require a global memory of places visited but is largely explained by purely local turning rules. By combining the classic labyrinth with modern methods of animal tracking and computational modeling one may gain a new view of learning and decision-making in biological agents.