Quantifying Extrinsic Curvature in Neural Manifolds

TL;DR

This paper introduces a novel method combining topological deep generative models and Riemannian geometry to explicitly parameterize neural manifolds and measure their extrinsic curvature, aiding understanding of neural data structure.

Contribution

It presents a new approach for estimating the shape and curvature of neural manifolds that is invariant to neuron permutation, validated on synthetic and real neural data.

Findings

Accurately estimates geometry of synthetic manifolds with noise

Recovers known geometric structures in hippocampal place cells

Method invariant to neuron permutation

Abstract

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In this work, we combine topological deep generative models and extrinsic Riemannian geometry to introduce a novel approach for studying the structure of neural manifolds. This approach (i) computes an explicit parameterization of the manifolds and (ii) estimates their local extrinsic curvature--hence quantifying their shape within the neural state space. Importantly, we prove that our methodology is invariant with respect to transformations that do not bear meaningful neuroscience information, such as permutation of the order in which neurons are recorded. We show empirically that we correctly estimate the geometry of synthetic manifolds generated from smooth deformations of circles, spheres, and tori, using realistic noise levels. We additionally validate our methodology on simulated and real neural data, and show that we recover geometric structure known to exist in hippocampal place cells. We expect this approach to open new avenues of inquiry into geometric neural correlates of perception and behavior.