ArticleProceedings. Biological sciences2016
A spiking neural model of adaptive arm control.
Article in Proceedings. Biological sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Closed-loop coupling of both physiological spindle model and spinal pathways for sensorimotor control of human center-out reaching.Frontiers in computational neuroscience · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Brain-inspired biomimetic robot control: a review.Frontiers in neurorobotics · 2024Review
- Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities.Sensors (Basel, Switzerland) · 2023Review
- Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.Patterns (New York, N.Y.) · 2022Article
- Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian.Frontiers in neuroinformatics · 2022Article
- Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.Frontiers in neuroinformatics · 2022Article
- Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning.Frontiers in neuroscience · 2022Article
- Article
- A leg to stand on: computational models of proprioception.Current opinion in physiology · 2021Article
- Neuromorphic NEF-Based Inverse Kinematics and PID Control.Frontiers in neurorobotics · 2021Article
- Article
- The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis.Journal of the Royal Society, Interface · 2020Article
- Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi.Frontiers in neurorobotics · 2020Article
- Perception Understanding Action: Adding Understanding to the Perception Action Cycle With Spiking Segmentation.Frontiers in neurorobotics · 2020Article
- Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics.Frontiers in neurorobotics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a spiking neuron model of the motor cortices and cerebellum of the motor control system. The model consists of anatomically organized spiking neurons encompassing premotor, primary motor, and cerebellar cortices. The model proposes novel neural computations within these areas to control a nonlinear three-link arm model that can adapt to unknown changes in arm dynamics and kinematic structure. We demonstrate the mathematical stability of both forms of adaptation, suggesting that this is a robust approach for common biological problems of changing body size (e.g. during growth), and unexpected dynamic perturbations (e.g. when moving through different media, such as water or mud). To demonstrate the plausibility of the proposed neural mechanisms, we show that the model accounts for data across 19 studies of the motor control system. These data include a mix of behavioural and neural spiking activity, across subjects performing adaptive and static tasks. Given this proposed characterization of the biological processes involved in motor control of the arm, we provide several experimentally testable predictions that distinguish our model from previous work.
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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.