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Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm.

Brendan Lenfesty1, Saugat Bhattacharyya2, KongFatt Wong-Lin3

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This study introduces sparse identification of nonlinear dynamics (SINDy) to model decision-making dynamics. SINDy effectively estimates decision variables and model parameters from neural activity, advancing perceptual decision research.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Dynamical Systems

Background:

  • Perceptual decision making relies on accumulating sensory evidence over time.
  • Sequential sampling models describe this process, with decision variables tracked by neural activity.
  • Current computational methods for analyzing decision dynamics are limited.

Purpose of the Study:

  • To apply sparse identification of nonlinear dynamics (SINDy) to uncover deterministic components of stochastic decision models.
  • To evaluate SINDy's effectiveness in estimating model parameters and predicting behavior from simulated neural data.
  • To explore SINDy's utility for analyzing perceptual decision-making dynamics.

Main Methods:

  • Utilized sparse identification of nonlinear dynamics (SINDy), a data-driven approach.
  • Applied SINDy to simulated decision variable activities from reaction time tasks.
  • Investigated multi-trial, trial-averaging, and single-trial SINDy approaches, assuming known noise coefficients.

Main Results:

  • SINDy successfully estimated deterministic terms in dynamical equations, choice accuracy, and decision time across various signal-to-noise ratios.
  • The multi-trial SINDy approach yielded the best performance.
  • Single-trial SINDy showed potential for real-time modeling.

Conclusions:

  • SINDy offers a powerful data-driven method for elucidating the dynamics of perceptual decision making.
  • The findings provide alternative approaches for analyzing first-passage time problems using SINDy.
  • This work advances computational methods for understanding neural mechanisms of choice.