Core Viewpoint - The rapid development of artificial intelligence (AI) technology is accompanied by increasing concerns over high energy consumption, leading to the exploration of Spiking Neural Networks (SNNs) as a more biologically plausible and energy-efficient computational paradigm [2][3]. Summary by Sections Current Challenges in SNNs - There is a lack of a spiking neuron model that effectively balances computational efficiency and biological plausibility, which is a key limitation for the development and application of SNNs [3]. - Existing spiking neuron models, such as Leaky Integrate-and-Fire (LIF), Adaptive LIF (ALIF), Hodgkin-Huxley (HH), and Multi-compartment models, primarily focus on simulating neuronal dynamic behavior and assume single-channel connections between neurons, leading to information loss in spatiotemporal tasks [3][9]. Introduction of Multi-Synaptic Firing Neuron Model - A new spiking neuron model called Multi-Synaptic Firing (MSF) neuron has been proposed, which can encode spatiotemporal information simultaneously without increasing computational delay or significantly raising power consumption [5][10]. - The MSF neuron model is inspired by the biological phenomenon of "multi-synaptic connections," allowing a single axon to establish multiple synapses with different firing thresholds on the same target neuron, a feature observed in various biological brains [9]. Theoretical and Experimental Findings - Theoretical analysis shows that the MSF neuron is a universal and more refined abstraction of neurons, with traditional LIF neurons and classic ReLU neurons being special cases under specific parameters, revealing the intrinsic connection between ANNs and SNNs [10]. - The study provides an optimal synaptic threshold selection scheme and an alternative parameter optimization criterion to avoid gradient vanishing or explosion issues during the training of deep SNNs, enabling scalability without performance degradation [10][13]. Performance and Applications - Experimental results demonstrate that the MSF neuron can simultaneously encode spatial intensity distribution and temporal dynamics through independent frequency and temporal coding methods, outperforming traditional LIF neurons in various benchmark tasks [13]. - In tasks involving continuous event streams, SNNs built on MSF neurons even surpassed ANNs with the same network structure, showcasing higher energy efficiency [13][14]. - The MSF neuron model has been successfully deployed on domestic neuromorphic hardware platforms, validating its compatibility in real-world scenarios such as event-driven object detection in autonomous driving [14][15]. Future Directions - The research team aims to explore the application potential of MSF neurons in a broader range of tasks, contributing to the advancement of AI technology towards more intelligent, green, and sustainable development [19].