[Sds-seminars] WTI Distinguished Speaker Series: Interpreting neural dynamics by modeling beliefs

[elizavette.torres at yale.edu]

Professor Xaq Pitkow will be speaking next week on "Interpreting neural
dynamics by modeling beliefs" as part of the Distinguished Speaker Series of
the Wu Tsai Institute at Yale University.


The talk will be held at 11:30am on November 3, 2022 in William L. Harkness
Hall, Room 117.



 [cid:e79036c1-747b-4309-be46-94d1dc1be086]

Abstract:

Complex behaviors are often driven by an internal model, which integrates
sensory information over time and facilitates long-term planning to reach
subjective goals. We interpret behavioral data by assuming an agent behaves
rationally --- that is, they take actions that optimize their subjective
reward according to their understanding of the task and its relevant causal
variables. We apply a new 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. Technically, we define an
animal's strategy as solving a Partially Observable Markov Decision Process
(POMDP), and we invert this model to find the task and subjective costs that
have maximum likelihood. This is a generalization of both Inverse
Reinforcement Learning and Inverse Optimal Control. Our mathematical
formulation thereby extracts rational and interpretable thoughts of the
agent from its behavior. We apply this method to behavioral data from
primates catching fireflies in virtual reality and use it to understand
properties of the mental model monkeys use to navigate by optic flow.

 The thoughts imputed to the animal can then serve as latent targets for
neural analyses. Using these targets, we provide a framework for
interpreting the linked processes of encoding, recoding, and decoding of
neural data in light of the rational model for behavior. We first
demonstrate the merits of this approach on synthetic neural data during a
foraging task. We then analyze real neural activity in primate prefrontal
cortex (PFC) and posterior parietal cortex (PPC) to discover computations
relevant to foraging tasks. In PFC, we find that reward dynamics are
represented in a subspace of the high-dimensional population activity and
predict animal's subsequent choice better than either the true experimental
variables or the raw neural responses. In PPC, we find representations of
latent navigation-relevant variables, and find that task manipulations alter
the coupling between neurons, suggesting that these interactions reflect the
mental model used to perform task-relevant computations. Overall, our
approach may identify explainable structure in complex neural activity
patterns. This framework lays a foundation for discovering how the brain
chooses to act using dynamic beliefs about the uncertain world.



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