Rational thoughts in neural codes

Zhengwei Wu, Minhae Kwon, Saurabh Daptardar, Paul Schrater, Xaq Pitkow

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is, How can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally-that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a method, inverse rational control (IRC), to learn an agent's internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding, and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the Markovian computational dynamics within the neural manifold that represent the task. This work lays a foundation for discovering how the brain represents and computes with dynamic latent variables.

Original languageEnglish (US)
Pages (from-to)29311-29320
Number of pages10
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number47
StatePublished - Nov 24 2020

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. We thank Dora Angelaki, Baptiste Caziot, Valentin Dragoi, Kresˇimir Josić, Zhe Li, Rajkumar Raju, and Neda Shahidi for useful discussions. Z.W., P.S., and X.P. were supported in part by BRAIN Initiative National Institutes of Health Grant 5U01NS094368. Z.W. and X.P. were supported in part by an award from the McNair Foundation. S.D. and X.P. were supported in part by the Simons Collaboration on the Global Brain Award 324143 and National Science Foundation (NSF) 1450923 BRAIN 43092. X.P. and M.K. were supported in part by NSF CAREER Award IOS-1552868.

Publisher Copyright:
© 2020 National Academy of Sciences. All rights reserved.


  • Cognition
  • Computation
  • Neural coding
  • Neuroscience
  • Rational


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