Jove
Visualize
Contact Us
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 Concept Videos

Cognitive Learning01:21

Cognitive Learning

504
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
504
Neural Circuits01:25

Neural Circuits

1.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.5K
Associative Learning01:27

Associative Learning

548
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
548
Observational Learning01:12

Observational Learning

292
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
292
Neural Regulation01:37

Neural Regulation

39.8K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.8K
Neuronal Communication01:28

Neuronal Communication

1.4K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Climate change exacerbates disparities of energy resilience in New York City.

Nature communications·2026
Same author

Integrated framework to study genomic surveillance of selective sweeps in multivariants dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Resource constrained learning over wireless networks.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

BeamNet: Unsupervised Beamforming for ISAC Systems Under Imperfect CSI.

Entropy (Basel, Switzerland)·2026
Same author

Low-Latency Realism Through Randomized Distributed Function Computations: A Shannon Theoretic Approach.

Entropy (Basel, Switzerland)·2026
Same author

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem.

Proceedings of the National Academy of Sciences of the United States of America·2025

Related Experiment Video

Updated: Sep 3, 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

10.4K

Lead federated neuromorphic learning for wireless edge artificial intelligence.

Helin Yang1,2, Kwok-Yan Lam3,4, Liang Xiao1

  • 1Department of Information and Communication Engineering, School of Informatics, Xiamen University, Xiamen, China.

Nature Communications
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel brain-inspired computing method for edge artificial intelligence (AI). The lead federated neuromorphic learning (LFNL) technique enhances energy efficiency and reduces data traffic for AI model training on edge devices.

More Related Videos

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing
07:13

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Published on: October 20, 2021

3.3K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

650

Related Experiment Videos

Last Updated: Sep 3, 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

10.4K
Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing
07:13

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Published on: October 20, 2021

3.3K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

650

Area of Science:

  • Artificial Intelligence
  • Neuromorphic Computing
  • Edge Computing

Background:

  • Wireless edge AI requires large datasets for training, posing challenges for resource-constrained devices.
  • Energy-demanding model training on edge devices limits the potential of AI applications.
  • Privacy concerns arise with centralized data collection for AI model training.

Purpose of the Study:

  • To propose a decentralized, energy-efficient, brain-inspired computing method for edge AI.
  • To enable collaborative global model training on edge devices while preserving user privacy.
  • To reduce computational latency and data traffic in federated learning scenarios.

Main Methods:

  • Development of a lead federated neuromorphic learning (LFNL) technique.
  • Utilizing spiking neural networks and brain-like biophysiological structures.
  • Implementing a decentralized approach for collaborative model training.

Main Results:

  • LFNL achieves comparable recognition accuracy to existing edge AI techniques, even with uneven data distribution.
  • Substantial reduction in data traffic (>3.5×) and computational latency (>2.0×).
  • Significant decrease in energy consumption (>4.5×) compared to standard federated learning, with minimal accuracy loss (≤1.5%).

Conclusions:

  • LFNL facilitates energy-efficient AI model training on resource-constrained edge devices.
  • The technique enhances privacy by enabling decentralized, collaborative learning.
  • LFNL shows significant promise for advancing brain-inspired computing and edge AI development.