Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control
Open accessAbstract
Significance: Nervous systems use highly effective layered architectures in the sensorimotor control system to minimize the harmful effects of delay and inaccuracy in biological components. To study what makes effective architectures, we develop a theoretical framework that connects the component speed–accuracy trade-offs (SATs) with system SATs and characterizes the system performance of a layered control system. We show that diversity in layers (e.g., planning and reflex) allows fast and accurate sensorimotor control, even when each layer uses slow or inaccurate components. We term such phenomena “diversity-enabled sweet spots (DESSs).” DESSs explain and link the extreme heterogeneities in axon sizes and numbers and the resulting robust performance in sensorimotor control.
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Classical control is centralized, but nature has mastered distributed control to fluidly coordinate bodies with many degrees of freedom.
“Classical control is centralized, but nature has mastered distributed control to fluidly coordinate bodies with many degrees of freedom (Nakahira et al., 2021; Li, 2022b).”
From Large Language Models and the Reverse Turing Test (2023) · cites this paper as Nakahira et al., 2021
The architectures of brains are layered, with each layer responsible for making decisions on different timescales in both sensory and motor systems.
“The architectures of brains are layered, with each layer responsible for making decisions on different timescales in both sensory and motor systems (Wang, 2022; Nakahira et al., 2021; Li, 2022b).”
From Large Language Models and the Reverse Turing Test (2023) · cites this paper as Nakahira et al., 2021
Cites
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081 · see citing works
Sterling, P., & Laughlin, S. (2015) · doi:10.7551/mitpress/9780262028707.001.0001 · Cited by 482 · cited 4× · see citing works
“number, resulting in different speed and accuracy in nerve signaling (2, 3).”
BackgroundInfluentialMetabolic cost as a unifying principle governing neuronal biophysics.
Hasenstaub, A., Otte, S., Callaway, E., & Sejnowski, T. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914886107 · Cited by 246 · see citing works
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Large Language Models and the Reverse Turing Test
Sejnowski, T. J. (2023) · Neural Computation · doi:10.1162/neco_a_01563 · Cited by 169 · see citing works
“Classical control is centralized, but nature has mastered distributed control to fluidly coordinate bodies with many degrees of freedom (Nakahira et al., 2021; Li, 2022b).”
BackgroundInfluentialSelf-citationIjspeert, A. J., & Daley, M. A. (2023) · Journal of Experimental Biology · doi:10.1242/jeb.245784 · Cited by 79 · see citing works
- Authors
- Nakahira, Y., Liu, Q., Sejnowski, T. J., & Doyle, J. C.
- Affiliations
- California Institute of Technology (US)
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037;
- Corresponding author
- Doyle, John C.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-05-28
- Volume
- 118
- Issue
- 22
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8179159
- OpenAlex
- W2974141429
- Cited by
- 49
- References
- 3
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 13 · 2023: 5 · 2022: 8 · 2021: 5 · 2020: 7
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- NSF | CISE | Division of Information and Intelligent Systems (IIS-1735004)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- layered control architecture, speed-accuracy trade-off, sensorimotor control, mountain biking, control delay, reflexive control, trajectory planning, error decomposition, neural control of movement
- MeSH terms
- Adult, Humans, Male, Models, Biological, Movement, Psychomotor Performance, Reaction Time
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