bioRxiv preprint doi: https://doi.org/10.1101/2021.01.14.426746; this version posted January 15, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available underManuscript aCC-BY 4.0 under International review license. 1 Mice in a labyrinth: Rapid learning, 2 sudden insight, and efficient exploration 3 Matthew Rosenberg1†, Tony Zhang1†, Pietro Perona2, Markus Meister1* *For correspondence:
[email protected] 4 1Division of Biology and Biological Engineering, Caltech, USA; 2Division of
[email protected] (MR); 5 Engineering and Applied Science, Caltech, USA (TZ);
[email protected] (PP);
[email protected] 6 (MM) †These authors contributed 7 Abstract Animals learn certain complex tasks remarkably fast, sometimes after a single equally to this work 8 experience. What behavioral algorithms support this efficiency? Many contemporary studies 9 based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete 10 learning. As an alternative, we study the unconstrained behavior of mice in a complex 11 labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in 12 the labyrinth makes ~2000 navigation decisions per hour. The animal quickly discovers the 13 location of a reward in the maze and executes correct 10-bit choices after only 10 reward 14 experiences – a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve 15 discontinuously from one minute to the next, suggesting moments of sudden insight about the 16 structure of the labyrinth.