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Related Experiment Video

Updated: Jul 2, 2025

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Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.

Youngeun Kim1, Adar Kahana2, Ruokai Yin1

  • 1Department of Electrical Engineering, Yale University, New Haven, CT, United States.

Frontiers in Neuroscience
|February 29, 2024
PubMed
Summary
This summary is machine-generated.

Skip connections in Spiking Neural Networks (SNNs) using Time-To-First-Spike (TTFS) coding can improve performance. A novel learnable delay in concatenation-based skip connections enhances information mixing for better results.

Keywords:
Spiking Neural Networkenergy-efficient deep learningevent-based processingimage recognitiontemporal coding

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Spiking Neural Networks (SNNs) offer energy efficiency by mimicking biological neurons using Time-To-First-Spike (TTFS) coding.
  • Skip connections are crucial in Artificial Neural Networks (ANNs) but their role in TTFS-coded SNNs requires investigation.

Purpose of the Study:

  • To analyze the impact of addition-based and concatenation-based skip connections on TTFS-coded SNNs.
  • To propose a novel method to improve information mixing in concatenation-based skip connections for TTFS SNNs.

Main Methods:

  • Investigated addition-based and concatenation-based skip connection architectures in TTFS SNNs.
  • Introduced a learnable delay mechanism for concatenation-based skip connections.
  • Conducted experiments on MNIST and Fashion-MNIST datasets.

Main Results:

  • Addition-based skip connections introduce spike timing delays.
  • Concatenation-based skip connections create time gaps, hindering information mixing.
  • The proposed learnable delay effectively bridges the time gap, improving information mixing.

Conclusions:

  • Skip connections can be beneficial for TTFS-coded SNNs, with careful architectural design.
  • The learnable delay approach enhances the efficacy of concatenation-based skip connections.
  • TTFS coding and skip connections show promise beyond image recognition, including scientific machine learning.