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A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics.

Liangwei Fan1, Hui Shen2, Xiangkai Lian1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.

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|August 4, 2025
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A new Multi-Synaptic Firing (MSF) neuron enhances spiking neural networks (SNNs) for better spatiotemporal data processing. MSF neurons improve accuracy and efficiency in neuromorphic computing tasks.

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Area of Science:

  • Neuromorphic computing
  • Computational neuroscience
  • Artificial intelligence

Background:

  • Spiking neural networks (SNNs) offer biological plausibility and computational power due to temporal dynamics.
  • Standard SNN neurons face challenges in simultaneously encoding complex spatiotemporal input dynamics.

Purpose of the Study:

  • Introduce the Multi-Synaptic Firing (MSF) neuron, inspired by biological multisynaptic connections.
  • Enable SNNs to jointly encode spatial intensity and temporal dynamics for enhanced performance.

Main Methods:

  • Propose the MSF neuron model with multiple synapses and varying thresholds on a postsynaptic neuron.
  • Derive optimal threshold selection and parameter optimization for surrogate gradients.
  • Implement and evaluate deep MSF-based SNNs on various benchmarks.

Main Results:

  • MSF neurons generalize Leaky Integrate-and-Fire (LIF) and ReLU neurons.
  • Achieve superior accuracy compared to LIF neurons while maintaining low power and latency.
  • Outperform ReLU neurons in event-driven tasks, demonstrating high execution efficiency.

Conclusions:

  • MSF neurons significantly advance neuromorphic computing capabilities.
  • Enable scalable deep SNNs for real-world spatiotemporal applications without performance loss.