@inproceedings{8bbf1333b60e4fcf9486f66a578fc8cb,
title = "A general recurrent state space framework for modeling neural dynamics during decision-making",
abstract = "An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a general framework for modeling neural activity during decisionmaking. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state space models, for which we introduce a scalable variational Laplace EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the responses of a set of parietal neurons than a single accumulator model, and we identified a variable lower boundary in the responses of a parietal neuron during a random dot motion task. We expect this framework will be useful for modeling neural dynamics in a variety of decision-making settings.",
author = "Zoltowski, {David M.} and Pillow, {Jonathan W.} and Linderman, {Scott W.}",
note = "Funding Information: the Global Brain (SCGB AWD543027), the NIH BRAIN initiative (NS104899 and R01EB026946), and a U19 NIH-NINDS BRAIN Initiative Award (5U19NS104648). S.W.L. was supported by NIH grants U19NS113201 and R01NS113119. Funding Information: We thank Jacob Yates and Alex Huk for sharing their LIP data and Jamie Roitman and Michael Shadlen for making their LIP data publicly available. We also thank Benjamin Antin for contributions to the vLEM implementation and Orren Karniol-Tambour for helpful discussions. D.M.Z. was supported by NIH grant T32MH065214. J.W.P. was supported by grants from the Simons Collaboration on Publisher Copyright: {\textcopyright} 2020 by the Authors All rights reserved.; 37th International Conference on Machine Learning, ICML 2020 ; Conference date: 13-07-2020 Through 18-07-2020",
year = "2020",
language = "English (US)",
series = "37th International Conference on Machine Learning, ICML 2020",
publisher = "International Machine Learning Society (IMLS)",
pages = "11616--11627",
editor = "Hal Daume and Aarti Singh",
booktitle = "37th International Conference on Machine Learning, ICML 2020",
}