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

Neural Circuits01:25

Neural Circuits

2.6K
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...
2.6K
Neural Regulation01:37

Neural Regulation

43.1K
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.
43.1K
Propagation of Action Potentials01:23

Propagation of Action Potentials

8.9K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
8.9K

You might also read

Related Articles

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

Sort by
Same author

PMSN: A Parallel Multi-Compartment Spiking Neuron for Multiscale Temporal Processing.

IEEE transactions on neural networks and learning systems·2026
Same author

Maternal cold exposure improves offspring metabolic health via a milk lithocholic acid-microbiota-Th17 axis.

NPJ biofilms and microbiomes·2026
Same author

Advances in intraoperative margin assessment for solid tumors: Toward a new era of personalized precision surgery.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2026
Same author

Straw Return and Tillage Regulate Soil N Pool via Modifying Soil Conditions and Bacterial Communities in Coastal Saline-Alkaline Land.

Microorganisms·2026
Same author

Enhancing X-ray Image Classification through Heterogeneous Federated Learning with Natural Image-Augmented Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

JMIR aging·2026

Related Experiment Video

Updated: Jan 16, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

12.2K

Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.

Yinsong Yan1, Qu Yang2, Yujie Wu3

  • 1Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Nature Communications
|September 30, 2025
PubMed
Summary

This study introduces Rhythm-Spiking Neural Networks (SNNs) that mimic brain oscillations for superior temporal processing and noise robustness. Rhythm-SNNs significantly reduce energy consumption while achieving state-of-the-art performance in complex tasks.

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.8K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.7K

Related Experiment Videos

Last Updated: Jan 16, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

12.2K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.8K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.7K

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • The brain's temporal processing relies on complex dynamics, a capability not fully replicated by current spiking neural networks (SNNs).
  • Existing SNNs struggle with temporal tasks and are susceptible to noise, limiting their practical applications.
  • Neural oscillations are a key mechanism in biological brains for efficient information processing.

Purpose of the Study:

  • To enhance the temporal processing capabilities and noise robustness of SNNs.
  • To develop a novel SNN architecture inspired by biological neural oscillations.
  • To reduce the energy consumption of SNNs for efficient neuromorphic computing.

Main Methods:

  • Introduced Rhythm-SNN, a novel SNN architecture.
  • Employed heterogeneous oscillatory signals to modulate spiking neuron activation frequencies.
  • Conducted extensive experiments and theoretical analyses on various temporal processing tasks.

Main Results:

  • Rhythm-SNN significantly reduced neuronal firing rates and enhanced temporal processing capabilities.
  • The proposed model demonstrated superior robustness against noise perturbations.
  • Achieved state-of-the-art performance across multiple tasks with markedly reduced energy costs.
  • Outperformed deep learning solutions in a neuromorphic noise suppression challenge, achieving significant energy reduction.

Conclusions:

  • Rhythm-SNN effectively leverages neural oscillation principles to overcome limitations in traditional SNNs.
  • The approach offers a promising direction for developing more efficient and robust neuromorphic systems.
  • Rhythm-SNN presents a significant advancement in energy-efficient AI for temporal processing and noise suppression.