A PROBABILISTIC FRAMEWORK FOR TASK-ALIGNED INTRA-AND INTER-AREA NEURAL MANIFOLD ESTIMATION

Abstract

Latent manifolds provide a compact characterization of neural population activity and of shared co-variability across brain areas. Nonetheless, existing statistical tools for extracting neural manifolds face limitations in terms of interpretability of latents with respect to task variables, and can be hard to apply to datasets with no trial repeats. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) explicitly partitions variability into private and shared sources which can themselves be subdivided in task-relevant and task irrelevant components, uses a realistic Poisson noise model, and introduces temporal smoothing of latent trajectories in the form of a Gaussian Process prior. This TAME-GP graphical model allows for robust estimation of task-relevant variability in local population responses, and of shared co-variability between brain areas. We demonstrate the efficiency of our estimator on within model and biologically motivated simulated data. We also apply it to several datasets of neural population recordings during behavior. Overall, our results demonstrate the capacity of TAME-GP to capture meaningful intra-and inter-area neural variability with single trial resolution.

1. INTRODUCTION

Systems neuroscience is gradually shifting from relatively simple and controlled tasks, to studying naturalistic closed-loop behaviors where no two observations (i.e.,"trials") are alike (Michaiel et al., 2020; Noel et al., 2021) . Concurrently, neurophysiological techniques are advancing rapidly (Stevenson & Kording, 2011; Angotzi et al., 2019; Boi et al., 2020) to allow recording from an ever-increasing number of simultaneous neurons (i.e., "neural populations") and across multiple brain areas. These trends lead to a pressing need for statistical tools that compactly characterize the statistics of neural activity within and across brain regions. Dimensionality reduction techniques are a popular tool for interrogating the structure of neural responses (Cunningham & Byron, 2014) . However, as neural responses are driven by increasingly complex task features, the main axes of variability extracted using these techniques often intermix task and nuisance variables, making them hard to interpret. Alternatively, dimensionality reduction techniques that do allow for estimating task-aligned axes of variability (Brendel et al., 2011; Semedo et al., 2019; Keeley et al., 2020; Glaser et al., 2020; Hurwitz et al., 2021) , do not apply to communication between brain areas, and/or necessitate trial repeat structure that does not occur in natural behavior. Here, we introduce a probabilistic approach for learning interpretable task-relevant neural manifolds that capture both intra-and inter-area neural variability with single trial resolution. Task Aligned Manifold Estimation with Gaussian Process priors (TAME-GP) incorporates elements of demixed

