Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Circuits

Neural Circuits

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...

You might also read

Related Articles

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

Sort by

Same author

ISTASTrack: Bridging ANN and SNN via ISTA Adapter for RGB-Event Tracking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Subparticle Operando Imaging for Probing Electrocatalytic Intermediates and Cation Effects.

Journal of the American Chemical Society·2026

Same author

Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature extraction.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

Adaptive spatiotemporal neural networks through complementary hybridization.

Nature communications·2024

Same author

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.

Nature communications·2024

Same author

Direct measurement of radial fluence distribution inside a femtosecond laser filament core.

Optics express·2020

Search research articles

Related Experiment Video

Updated: May 5, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

Temporal local attention with adaptive decoding: Enhancing spiking neural networks for temporal computing

Hanxiao Fan¹, Hanle Zheng¹, Zikai Wang²

¹Center for Brain-Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 15, 2026

Summary

This summary is machine-generated.

Spiking neural networks (SNNs) struggle with long sequences. Temporal local attention (TLA) and adaptive decoding (AD) enhance SNNs for temporal computing, improving performance and training speed.

Keywords:

Adaptive decoding Sequence learning Spiking neural network Temporal local attention

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

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

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

Published on: November 12, 2019

Related Experiment Videos

Last Updated: May 5, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

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

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

Published on: November 12, 2019

Area of Science:

Artificial Intelligence
Computational Neuroscience

Background:

Spiking neural networks (SNNs) offer potential for complex learning and temporal computing due to their brain-inspired dynamics and energy efficiency.
However, SNNs face challenges in long-sequence gradient propagation, limiting their effectiveness in capturing temporal features.

Purpose of the Study:

To introduce novel mechanisms for enhancing SNN performance in temporal computing tasks.
To address limitations in SNN training and inference, particularly concerning long sequences and gradient propagation.

Main Methods:

Introduction of Temporal Local Attention (TLA) to reduce sequence length and improve gradient flow.
Incorporation of Adaptive Decoding (AD) to optimize SNN output based on performance correlations.
Integration of TLA and AD (TLA-AD) for comprehensive SNN modeling and training.

Main Results:

The TLA-AD method significantly enhances SNN performance and accelerates training without increasing parameter count.
Achieved state-of-the-art accuracy on the DEAP dataset (93.52% valence, 93.41% arousal).
Demonstrated competitive accuracy on SEED (87.41%) and IMDB (86.31%) datasets compared to other SNN methods.

Conclusions:

TLA-AD provides effective optimization strategies for SNNs in temporal computing.
The proposed methods overcome key limitations in SNN training and performance.
This work paves the way for more advanced and efficient SNN applications in time-series data analysis.