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Spiking neural network with working memory can integrate and rectify spatiotemporal features.

Yi Chen1, Hanwen Liu1, Kexin Shi1

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.

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Spiking neural networks (SNNs) have limited temporal sensitivity. We introduce Spiking Neural Networks with Working Memory (SNNWM) to improve global information processing and achieve state-of-the-art results on temporal tasks.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spiking neural networks (SNNs) excel in low-power computing and temporal tasks due to their unique dynamics.
  • Current SNNs exhibit limited temporal sensitivity, hindering their ability to process global information and reducing scalability.

Purpose of the Study:

  • To address the limited temporal sensitivity of SNNs by integrating a working memory mechanism.
  • To enhance SNNs' capacity for processing global information and improve performance on diverse datasets.

Main Methods:

  • Proposed Spiking Neural Networks with Working Memory (SNNWM) to process input spike trains segment by segment.
  • Developed biologically plausible and neuromorphic hardware-friendly implementation methods for SNNWM.
  • Integrated concepts from neuroscience, including working memory and delayed synapses.

Main Results:

  • SNNWM demonstrated improved processing of entire spike trains, overcoming limitations of short-term temporal focus.
  • The model achieved state-of-the-art performance, particularly in tasks requiring processing over short time steps.
  • Experimental results validated the effectiveness of the proposed working memory integration.

Conclusions:

  • Introducing working memory and delayed synapses significantly enhances SNN capabilities.
  • SNNWM offers a promising new perspective for designing advanced SNNs with improved temporal information processing.
  • The findings suggest broader applications for SNNs in complex, time-dependent real-world tasks.