<p>The neural representations of prior information about the state of the world are poorly understood<sup><CitationRef CitationID="CR1">1</CitationRef></sup>. 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.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

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 A. Catarino,
  • Gaelle A. Chapuis,
  • Anne K. Churchland,
  • Yang Dan,
  • Felicia Davatolhagh,
  • Eric E. J. DeWitt,
  • Tatiana A. Engel,
  • Michele Fabbri,
  • Mayo A. Faulkner,
  • Ila Rani Fiete,
  • Laura Freitas-Silva,
  • Berk Gerçek,
  • Kenneth D. Harris,
  • Michael Häusser,
  • Sonja B. Hofer,
  • Fei Hu,
  • Julia M. Huntenburg,
  • Anup Khanal,
  • Chris Krasniak,
  • Christopher Langdon,
  • Christopher A. Langfield,
  • Peter E. Latham,
  • Petrina Y. P. Lau,
  • Zach Mainen,
  • Guido T. Meijer,
  • Nathaniel J. Miska,
  • Thomas D. Mrsic-Flogel,
  • Jean-Paul Noel,
  • Kai Nylund,
  • Alejandro Pan-Vazquez,
  • Liam Paninski,
  • Jonathan Pillow,
  • Cyrille Rossant,
  • Noam Roth,
  • Rylan Schaeffer,
  • Michael Schartner,
  • Yanliang Shi,
  • Karolina Z. Socha,
  • Nicholas A. Steinmetz,
  • Karel Svoboda,
  • Charline Tessereau,
  • Anne E. Urai,
  • Miles J. Wells,
  • Steven Jon West,
  • Matthew R. Whiteway,
  • Olivier Winter,
  • Ilana B. Witten,
  • Anthony Zador,
  • Yizi Zhang,
  • Peter Dayan,
  • Alexandre Pouget

摘要

The neural representations of prior information about the state of the world are poorly understood1. 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.