Brain-wide representations of prior information in mouse decision-making
Charles Findling, Félix Hubert, Luigi Acerbi, Brandon Benson, Julius Benson, Daniel Birman, Niccolò Bonacchi, E. Kelly Buchanan, Sebastian Bruijns, Matteo Carandini, Joana Catarino, Gaëlle Chapuis, Anne K. Churchland, Yang Dan, M. Felicia Davatolhagh, Eric DeWitt, Tatiana A. Engel, Michele Fabbri, Mayo Faulkner, Ila Fiete, Laura Freitas-Silva, Berk Gerçek, Kenneth D. Harris, Michael Häusser, Sonja B. Hofer, Fei Hu, Julia M. Huntenburg, Anup Khanal, Christopher Krasniak, Christopher Langdon, Christopher Langfield, Peter E. Latham, Petrina Lau, Zachary F. Mainen, Guido T. Meijer, Nathaniel J Miska, Thomas D. Mrsic‐Flogel, Jean‐Paul Noel, Kai Nylund, Alejandro Pan-Vazquez, Liam Paninski, Jonathan W. Pillow, Cyrille Rossant, Noam Roth, Rylan Schaeffer, Michael Schartner, Yan-Liang Shi, Karolina Socha, Nicholas A. Steinmetz, Karel Svoboda, Charline Tessereau, Anne E Urai, Miles J. Wells, Steven J. West, Matthew R Whiteway, Olivier Winter, Ilana B. Witten, Anthony M. Zador, Yizi Zhang, Peter Dayan, Alexandre Pouget
Abstract
. Here, to investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with a prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions that, notably, span all levels of processing, from early sensory areas (the lateral geniculate nucleus and primary visual cortex) to motor regions (secondary and primary motor cortex and gigantocellular reticular nucleus) and high-level cortical regions (the dorsal anterior cingulate area and ventrolateral orbitofrontal cortex). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers a brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large-scale recordings on a single standardized task.