ArticleBiological cybernetics2017
Robustness, flexibility, and sensitivity in a multifunctional motor control model.
Article in Biological cybernetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- NEURONpyxl: fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses.Frontiers in computational neuroscience · 2026Article
- Tradeoffs in the energetic value of neuromodulation in a closed-loop neuromechanical system.Journal of theoretical biology · 2025Article
- Augmenting flexibility: mutual inhibition between inhibitory neurons expands functional diversity.iScience · 2025Article
- Variational analysis of sensory feedback mechanisms in powerstroke-recovery systems.Biological cybernetics · 2024Article
- A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network.Biological cybernetics · 2024Article
- Tutorial: using NEURON for neuromechanical simulations.Frontiers in computational neuroscience · 2023Article
- Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems.Biological cybernetics · 2022Article
- Cooperative stability renders protein complex formation more robust and controllable.Scientific reports · 2022Article
- Sensory feedback expands dynamic complexity and aids in robustness against noise.Biological cybernetics · 2022Article
- Quantitative comparison of the mean-return-time phase and the stochastic asymptotic phase for noisy oscillators.Biological cybernetics · 2022Article
- A homeostasis criterion for limit cycle systems based on infinitesimal shape response curves.Journal of mathematical biology · 2022Article
- Article
- Dynamical consequences of sensory feedback in a half-center oscillator coupled to a simple motor system.Biological cybernetics · 2021Article
- Sharpened and Mechanically Durable Carbon Fiber Electrode Arrays for Neural Recording.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2021Article
- Shape versus timing: linear responses of a limit cycle with hard boundaries under instantaneous and static perturbation.SIAM journal on applied dynamical systems · 2021Article
- Control for multifunctionality: bioinspired control based on feeding in Aplysia californica.Biological cybernetics · 2020Article
- Computational model of the distributed representation of operant reward memory: combinatoric engagement of intrinsic and synaptic plasticity mechanisms.Learning & memory (Cold Spring Harbor, N.Y.) · 2020Article
- Propensity for Bistability of Bursting and Silence in the Leech Heart Interneuron.Frontiers in computational neuroscience · 2018Article
- Eupnea, tachypnea, and autoresuscitation in a closed-loop respiratory control model.Journal of neurophysiology · 2017Article
- Rapid Adaptation to Changing Mechanical Load by Ordered Recruitment of Identified Motor Neurons.eNeuroArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motor systems must adapt to perturbations and changing conditions both within and outside the body. We refer to the ability of a system to maintain performance despite perturbations as "robustness," and the ability of a system to deploy alternative strategies that improve fitness as "flexibility." Different classes of pattern-generating circuits yield dynamics with differential sensitivities to perturbations and parameter variation. Depending on the task and the type of perturbation, high sensitivity can either facilitate or hinder robustness and flexibility. Here we explore the role of multiple coexisting oscillatory modes and sensory feedback in allowing multiphasic motor pattern generation to be both robust and flexible. As a concrete example, we focus on a nominal neuromechanical model of triphasic motor patterns in the feeding apparatus of the marine mollusk Aplysia californica. We find that the model can operate within two distinct oscillatory modes and that the system exhibits bistability between the two. In the "heteroclinic mode," higher sensitivity makes the system more robust to changing mechanical loads, but less robust to internal parameter variations. In the "limit cycle mode," lower sensitivity makes the system more robust to changes in internal parameter values, but less robust to changes in mechanical load. Finally, we show that overall performance on a variable feeding task is improved when the system can flexibly transition between oscillatory modes in response to the changing demands of the task. Thus, our results suggest that the interplay of sensory feedback and multiple oscillatory modes can allow motor systems to be both robust and flexible in a variable environment.
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