ArticleProceedings of the National Academy of Sciences of the United States of America2018
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 3 citations in OpenAlex.
- Structural dynamics and neural representation of wing deformation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-By-Feel: Bioinspired Approach and Application.Biomimetics (Basel, Switzerland) · 2024Article
- Intraspecific Variation in the Placement of Campaniform Sensilla on the Wings of the HawkmothIntegrative organismal biology (Oxford, England) · 2024Article
- Understanding the Role of Sensor Optimisation in Complex Systems.Sensors (Basel, Switzerland) · 2023Review
- Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward.Integrative and comparative biology · 2023Review
- An information theoretic method to resolve millisecond-scale spike timing precision in a comprehensive motor program.PLoS computational biology · 2023Article
- Nonuniform structural properties of wings confer sensing advantages.Journal of the Royal Society, Interface · 2023Article
- Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices.BME frontiers · 2023Review
- Derivative-Free Observability Analysis for Sensor Placement Optimization of Bioinspired Flexible Flapping Wing System.Biomimetics (Basel, Switzerland) · 2022Article
- Structured random receptive fields enable informative sensory encodings.PLoS computational biology · 2022Article
- Systematic characterization of wing mechanosensors that monitor airflow and wing deformations.iScience · 2022Article
- Sparse pixel image sensor.Scientific reports · 2022Article
- Wing structure and neural encoding jointly determine sensing strategies in insect flight.PLoS computational biology · 2021Article
- Coriolis and centrifugal forces drive haltere deformations and influence spike timing.Journal of the Royal Society, Interface · 2019Article
- Making sense of sparse data with neural encoding strategies.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Sparse sensor placement is a central challenge in the efficient characterization of complex systems when the cost of acquiring and processing data is high. Leading sparse sensing methods typically exploit either spatial or temporal correlations, but rarely both. This work introduces a sparse sensor optimization that is designed to leverage the rich spatiotemporal coherence exhibited by many systems. Our approach is inspired by the remarkable performance of flying insects, which use a few embedded strain-sensitive neurons to achieve rapid and robust flight control despite large gust disturbances. Specifically, we identify neural-inspired sensors at a few key locations on a flapping wing that are able to detect body rotation. This task is particularly challenging as the rotational twisting mode is three orders of magnitude smaller than the flapping modes. We show that nonlinear filtering in time, built to mimic strain-sensitive neurons, is essential to detect rotation, whereas instantaneous measurements fail. Optimized sparse sensor placement results in efficient classification with approximately 10 sensors, achieving the same accuracy and noise robustness as full measurements consisting of hundreds of sensors. Sparse sensing with neural-inspired encoding establishes an alternative paradigm in hyperefficient, embodied sensing of spatiotemporal data and sheds light on principles of biological sensing for agile flight control.
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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.