Manifold learning for olfactory habituation to strongly fluctuating backgrounds

bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-30 · 2 citations

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 IBCM or online PCA 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 towards distinguishing plasticity rules in experiments and could be leveraged by other biological circuits facing fluctuating environments.

Rapid transcriptional response to a dynamic morphogen by time integration

bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-09

Abstract During development, cells must interpret extracellular signals with speed and accuracy. While morphogen gradients pattern tissues, how cells respond to dynamic morphogens remains unclear. Here, we investigate how dorsal patterning in the Drosophila embryo is specified by a rapidly evolving BMP gradient. Using a live reporter of BMP pathway activity and nascent transcription reporters, we find that gene expression is best predicted by time integration of BMP signaling, rather than instantaneous levels. However, in sog mutant embryos with broad BMP activity, integration alone fails to predict gene expression outside the normal domain. We show that the transcription factor Zen lowers the signaling threshold required for activation, enabling integration to drive rapid transcriptional responses even at low BMP levels. Together, these results suggest that cells interpret dynamic morphogen signals through the combined action of temporal integration and spatial competence, providing a framework for robust pattern formation on fast developmental timescales.

Optimal trajectories for Bayesian olfactory search in turbulent flows: The low information limit and beyond

Physical Review Fluids · 2025-04-09 · 3 citations

Certain animals have evolved complex strategies to track sources of odors which are advected by turbulent flows. In this paper, we model this search task as a partially observable Markov decision process, which allows us to compute optimal Bayesian search strategies in the sense that they reach the source in minimal average time. We apply this approach to realistic data taken from direct numerical simulation. Focusing on the especially difficult decision of what to do when contact with the odor has been lost, we study the optimal trajectories in this scenario --- which strongly resemble known animal behaviors --- and try to understand the results by way of a simplified model.

Exploring Bayesian olfactory search in realistic turbulent flows

Physical Review Fluids · 2025-06-03 · 4 citations

The problem of tracking the source of a passive scalar in a turbulent flow is relevant to flying insect behavior and several other applications. Extensive previous work has shown that certain Bayesian strategies, such as "infotaxis," can be very effective for this difficult "olfactory search" problem. More recently, a quasioptimal Bayesian strategy was computed under the assumption that encounters with the scalar are independent. However, the Bayesian approach has not been adequately studied in realistic flows which exhibit spatiotemporal correlations. In this work, we perform direct numerical simulations (DNSs) of an incompressible flow at Re-lambda similar or equal to 150, while tracking Lagrangian particles that are emitted by a point source and imposing a uniform mean flow with several magnitudes (including zero). We extract the spatially dependent statistics of encounters with the particles, which we use to build Bayesian policies, including generalized ("space-aware") infotactic heuristics and quasioptimal policies. We then assess the relative performance of these policies when they are used to search using scalar cue data from the DNSs, and in particular we study how this performance depends on correlations between encounters. Among other results, we find that quasioptimal strategies continue to outperform heuristics in the presence of strong mean flow but fail to do so in the absence of a mean flow. We also explore how to choose optimal search parameters, including the frequency and threshold concentration of observation.

Mice navigate scent trails using predictive policies

bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-01 · 3 citations

Animals actively sense their environment to extract features of interest to guide behaviors. For mammals, odors are prominent environmental features which are sampled by active modulation of sniffing and orofacial orientation. We sought to understand the strategies that mice use to navigate surface-bound odor cues. We presented mice with dynamic, non-repeating odor trails using a paper treadmill, and observed their behaviors as they collected rewards offered randomly along the trail. By combining high-speed videography over long distances with quantitative behavioral analyses, we find that mice rapidly learn to track odor trails persistently and precisely. Mice with a single nostril blocked can track odor trails, but with a lateral bias and lower precision than control animals. Tracking is severely impaired in animals with both nostrils intact but with interhemispheric communication disrupted by anterior commissure transection. Respiration measurements revealed that a sniff close to the trail triggers a rapid turn towards the trail, a reaction that is lost in commissure-cut animals. Importantly, trail tracking is not simply reactive but involves adaptation to and retention of a short-term memory of the trail geometry and statistics. Our results, recapitulated by a Bayesian inference model, indicate that mice combine immediate sensory information with an internal model of the odor environment to follow odor trails efficiently.

Defects, Parcellation, and Renormalized Negative Diffusivities in Nonhomogeneous Oscillatory Media

Physical Review Letters · 2025-10-14

Spatial nonhomogeneities can synchronize clusters of spatially extended oscillators in different frequency plateaus. Motivated by physiological rhythms, we fully characterize the phase diagram of a Ginzburg-Landau (GL) model with a gradient of frequencies. For large gradients and diffusion, the rest state is stable, and the linear spectrum around it maps onto the non-Hermitian Bloch-Torrey equation. When complex pairs of eigenvalues turn unstable, precursors of plateaus grow, separated by defects where the GL amplitude vanishes. Nonlinear effects either saturate the amplitude of plateaus or lead to a phase-locked state, with saddle-node bifurcations separating the two regimes. In the region of plateaus, we trace the formation of defects to a nonlinear renormalization of the diffusivity, and determine the scaling of their number and length vs dynamical parameters.

Defects, parcellation, and renormalized negative diffusivities in non-homogeneous oscillatory media

ArXiv.org · 2025-02-13

Spatial non-homogeneities can synchronize clusters of spatially-extended oscillators in different frequency plateaus. Motivated by physiological rhythms, we fully characterize the phase diagram of a Ginzburg-Landau (GL) model with a gradient of frequencies. For large gradients and diffusion, the rest state is stable, and the linear spectrum around it maps onto the non-Hermitian Bloch-Torrey equation. When complex pairs of eigenvalues turn unstable, precursors of plateaus grow, separated by defects where the GL amplitude vanishes. Nonlinear effects either saturate the amplitude of plateaus or lead to a phase-locked state, with saddle-node bifurcations separating the two regimes. In the region of plateaus, we trace the formation of defects to a non-linear renormalization of the diffusivity, and determine the scaling of their number and length vs dynamical parameters.

Topological interactions drive the first fate decision in the Drosophila embryo

Nature Physics · 2025-02-25 · 3 citations

Decaying and expanding Erk gradients process memory of skeletal size during zebrafish fin regeneration

bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-23 · 8 citations

Abstract Regeneration of an amputated salamander limb or fish fin restores pre-injury size and structure, illustrating the phenomenon of positional memory. Although appreciated for centuries, the identity of position-dependent cues and how they control tissue growth are not resolved. Here, we quantify Erk signaling events in whole populations of osteoblasts during zebrafish fin regeneration. We find that osteoblast Erk activity is dependent on Fgf receptor signaling and organized into millimeter-long gradients that extend from the distal tip to the amputation site. Erk activity scales with the amount of tissue amputated, predicts the likelihood of osteoblast cycling, and predicts the size of regenerated skeletal structures. Mathematical modeling suggests gradients are established by the transient deposition of long-lived ligands that are transported by tissue growth. This concept is supported by the observed scaling of expression of the essential epidermal ligand fgf20a with extents of amputation. Our work provides evidence that localized, scaled expression of pro-regenerative ligands instructs long-range signaling and cycling to control skeletal size in regenerating appendages.

Fast decisions with biophysically constrained gene promoter architectures

ArXiv.org · 2025-07-04

Cells integrate signals and make decisions about their future state in short amounts of time. A lot of theoretical effort has gone into asking how to best design gene regulatory circuits that fulfill a given function, yet little is known about the constraints that performing that function in a small amount of time imposes on circuit architectures. Using an optimization framework, we explore the properties of a class of promoter architectures that distinguish small differences in transcription factor concentrations under time constraints. We show that the full temporal trajectory of gene activity allows for faster decisions than its integrated activity represented by the total number of transcribed mRNA. The topology of promoter architectures that allow for rapidly distinguishing low transcription factor concentrations result in a low, shallow, and non cooperative response, while at high concentrations, the response is high and cooperative. In the presence of non-cognate ligands, networks with fast and accurate decision times need not be optimally selective, especially if discrimination is difficult. While optimal networks are generically out of equilibrium, the energy associated with that irreversibility is only modest, and negligible at small concentrations. Instead, our results highlight the crucial role of rate-limiting steps imposed by biophysical constraints.