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Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows

Peter Bouss1,2,*,†, Sandra Nestler1,2,3,*, Kirsten Fischer1,2, Claudia Merger1,4,5, Lars Schutzeichel1,2,6, Simon Musall6,7,8, Alexandre René1,9,10, and Moritz Helias1,4

  • *These authors contributed equally to this work.
  • †Contact author: p.bouss@fz-juelich.de
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PRX Life 4, 023009 – Published 27 April, 2026

DOI: https://doi.org/10.1103/rmfp-b7b5

Abstract

Neuronal activity is found to lie on low-dimensional manifolds embedded within the high-dimensional neuron space. Variants of principal component analysis are frequently employed to assess these manifolds. These methods are, however, limited by assuming a Gaussian data distribution and a flat manifold. In this study, we introduce a method designed to satisfy three core objectives: (1) extract coordinated activity across neurons, described either statistically as correlations or geometrically as manifolds; (2) identify a small number of latent variables capturing these structures; and (3) offer an analytical and interpretable framework characterizing statistical properties by a characteristic function and describing manifold geometry through a collection of charts. To this end, we employ normalizing flows (NFs), which learn an underlying probability distribution of data by an invertible mapping between data and latent space. Their simplicity and ability to compute exact likelihoods distinguish them from other generative networks. We adjust the NF's training objective to distinguish between relevant (in manifold) and noise dimensions (out of manifold). Additionally, we find that different behavioral states align with the components of the latent Gaussian mixture model, enabling their treatment as distinct curved manifolds. Subsequently, we approximate the network for each mixture component with a quadratic mapping, allowing us to characterize both neural manifold curvature and non-Gaussian correlations among recording channels. Applying the method to recordings in macaque visual cortex, we demonstrate that state-dependent manifolds are curved and exhibit complex statistical dependencies. Our approach thus enables an expressive description of neural population activity, uncovering nonlinear interactions among groups of neurons.

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PRX Life 4, 023009– Published 27 April, 2026
  • Received 20 June 2025
  • Accepted 19 March 2026
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