A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics

Liangwei Fan, Hui Shen (), Xiangkai Lian, Yulin Li, Man Yao, Guoqi Li () and Dewen Hu ()
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Liangwei Fan: National University of Defense Technology
Hui Shen: National University of Defense Technology
Xiangkai Lian: National University of Defense Technology
Yulin Li: National University of Defense Technology
Man Yao: Chinese Academy of Sciences
Guoqi Li: Chinese Academy of Sciences
Dewen Hu: National University of Defense Technology

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Spiking neural networks (SNNs) are biologically more plausible and computationally more powerful than artificial neural networks due to their intrinsic temporal dynamics. However, vanilla spiking neurons struggle to simultaneously encode spatiotemporal dynamics of inputs. Inspired by biological multisynaptic connections, we propose the Multi-Synaptic Firing (MSF) neuron, where an axon can establish multiple synapses with different thresholds on a postsynaptic neuron. MSF neurons jointly encode spatial intensity via firing rates and temporal dynamics via spike timing, and generalize Leaky Integrate-and-Fire (LIF) and ReLU neurons as special cases. We derive optimal threshold selection and parameter optimization criteria for surrogate gradients, enabling scalable deep MSF-based SNNs without performance degradation. Extensive experiments across various benchmarks show that MSF neurons significantly outperform LIF neurons in accuracy while preserving low power, low latency, and high execution efficiency, and surpass ReLU neurons in event-driven tasks. Overall, this work advances neuromorphic computing toward real-world spatiotemporal applications.

Date: 2025
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DOI: 10.1038/s41467-025-62251-6

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