Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm | Neural Computation | MIT Press
Brendan Lenfesty, Saugat Bhattacharyya, KongFatt Wong-Lin; Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm. Neural Comput 2025; 37 (3): 569–587. doi: https://doi.org/10.1162/neco_a_01736 Download citation file: Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-drive