ArticleNature communications2025
A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Deciphering optical coupled resonant systems with physics-data co-driven deep neural networks.Light, science & applications · 2026Article
- Multi-Physical Field Modulated P-Bit Device Based on VOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
- Spiking Neural Networks in Imaging: A Review and Case Study.Sensors (Basel, Switzerland) · 2025Review
- Efficient and real-time perception: a survey on end-to-end event-based object detection in autonomous driving.Frontiers in robotics and AI · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.