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Manifold Learning for Olfactory Habituation to Strongly Fluctuating Backgrounds

François X. P. Bourassa1,2,3,*, Paul François3,4, Gautam Reddy1,†, and Massimo Vergassola5,6

  • *Contact author: fb3@princeton.edu
  • †Contact author: greddy@princeton.edu
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PRX Life 4, 013008 – Published 16 January, 2026

DOI: https://doi.org/10.1103/q62z-7j1d

Abstract

Animals rely on their sense of smell to survive, but important olfactory cues are mixed with confounding background odors that fluctuate due to atmospheric turbulence. It is unclear how the olfactory system habituates to such stochastic backgrounds to detect behaviorally important odors. Here, we explicitly consider the high-dimensional nature of odor coding, the natural statistics of odor fluctuations, and the architecture of the early olfactory pathway. We show that their combination favors a manifold learning mechanism for olfactory habituation over alternatives based on predictive filtering. Manifold learning is implemented in our model by a biologically plausible network of inhibitory interneurons in the early olfactory pathway. We demonstrate that plasticity rules based on the Intrator, Bienenstock, Cooper, and Munro (IBCM) model or an online principal components analysis algorithm are effective at implementing this mechanism in turbulent conditions and outperform previous models relying on mean background subtraction. Interneurons with an IBCM plasticity rule acquire selectivity to independently varying odors. This manifold learning mechanism offers a path toward distinguishing plasticity rules in experiments and could be leveraged by other biological circuits facing fluctuating environments.

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PRX Life Collection on adaptation and learning

PRX Life presents a special collection on ‘Biological systems that learn’, curated by the organizers of the National Institute for Theory and Mathematics in Biology (NITMB) workshop together with early-career researchers. The papers in this collection illustrate how living systems acquire functions, showing it is useful to describe them as tunable matter with many individually adjustable degrees of freedom characterizing microscopic properties. Drawing inspiration from AI and non-biological systems that can acquire desired functions, the featured research highlights the timeliness of the topic and its potential to drive unifying frameworks for emergent collective function in living tunable matter.

Every article in this collection is subject to a rigorous peer review process, upholding the high standards applied to all papers in the journal. The PRX Life editorial team manages peer review and makes all editorial decisions.

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PRX Life 4, 013008– Published 16 January, 2026
  • Received 15 May 2025
  • Accepted 1 December 2025
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