Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Efficient Multitask Onboard Vision Sensing for Open-Pit Mining Advanced Driver Assistance System with Classification-Guided Adaptive Temporal Inference.

Sensors (Basel, Switzerland)·2026
Same author

Extended Dynamic Model for the UR16e 6-Degree-of-Freedom Robotic Manipulator.

Sensors (Basel, Switzerland)·2025
Same author

Intelligent Fault-Tolerant Control of Delta Robots: A Hybrid Optimization Approach for Enhanced Trajectory Tracking.

Sensors (Basel, Switzerland)·2025
Same author

Advances in Deep Learning for Semantic Segmentation of Low-Contrast Images: A Systematic Review of Methods, Challenges, and Future Directions.

Sensors (Basel, Switzerland)·2025
Same author

Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance.

Biomedicines·2024
Same author

Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 5, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.

Dailin Marrero1, John Kern1, Claudio Urrea1

  • 1Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile (USACH), Av. Víctor Jara 3519, Estación Central, Santiago 9170124, Chile.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

Spiking neural networks (SNNs) enhance robotic arm trajectory tracking. This brain-inspired approach offers superior accuracy and efficiency over traditional controllers.

Keywords:
NEFNengorobotic controlspiking neural networks

More Related Videos

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K

Related Experiment Videos

Last Updated: Jul 5, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K

Area of Science:

  • Robotics
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spiking neural networks (SNNs) mimic brain function using temporal coding for efficient information processing.
  • Conventional neural networks struggle with temporal information critical for robotic control.
  • SNNs offer potential advantages in adaptability and efficiency for robotic applications.

Purpose of the Study:

  • To investigate SNNs for developing advanced robotic controllers.
  • To improve trajectory tracking accuracy in robotic arms using SNNs.
  • To design and simulate a novel SNN-based controller.

Main Methods:

  • Employed the Neural Engineering Framework (NEF) for controller design.
  • Developed a spiking Proportional-Integral-Derivative (PID) controller.
  • Simulated the controller for a 3-Degrees-of-Freedom (3-DoF) robotic arm using Nengo and MATLAB R2022b.

Main Results:

  • The SNN-based controller demonstrated high accuracy and efficiency in trajectory tracking.
  • Achieved minimal deviations, overshoots, and oscillations during trajectory following.
  • Outperformed a fuzzy controller by 5% and a conventional PID controller by 6% (ITAE) and 30% (RMSE).

Conclusions:

  • NEF and SNNs are effective for developing robotic controllers.
  • SNN-based controllers show significant potential for robotic arm trajectory tracking.
  • This research paves the way for SNNs in dynamic environments and advanced robotics.