ArticleNature machine intelligence2023
Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation.Sensors (Basel, Switzerland) · 2026Article
- Predictive pursuit emerges in high-dimensional recurrent neural networks.bioRxiv : the preprint server for biology · 2026Article
- Wind history shapes olfactory search response in free flyingbioRxiv : the preprint server for biology · 2026Article
- TURB-Smoke. A database of Lagrangian pollutants emitted from point sources in turbulent flows with a mean wind.Scientific data · 2026Article
- InputDSA : Demixing then comparing recurrent and externally driven dynamics.... International Conference on Learning Representations · 2026Article
- Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2026Article
- Neural basis of cooperative behavior in biological and artificial intelligence systems.Science (New York, N.Y.) · 2026Article
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- Trajectory planning for drone landing, incorporating wind-sensing capabilities, operational and safety objectives, and reinforcement learning.Communications engineering · 2025Article
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- Multispecies sensory networks and social foraging strategies: Implications for population decline in procellariiform seabirds.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Mice navigate scent trails using predictive policies.bioRxiv : the preprint server for biology · 2025Article
- Article
- Optimal trajectories for Bayesian olfactory search in turbulent flows: The low information limit and beyond.Physical review fluids · 2025Article
- Sensing flow gradients is necessary for learning autonomous underwater navigation.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents' emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.