Author name cluster

Runhao Jiang

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization

Runhao Jiang
Chengzhi Jiang
Rui Yan
Huajin Tang

The surrogate gradient (SG) method has shown significant promise in enhancing the performance of deep spiking neural networks (SNNs), but it also introduces vulnerabilities to adversarial attacks. Although spike coding strategies and neural dynamics parameters have been extensively studied for their impact on robustness, the critical role of gradient magnitude, which reflects the model's sensitivity to input perturbations, remains underexplored. In SNNs, the gradient magnitude is primarily determined by the interaction between the membrane potential distribution (MPD) and the SG function. In this study, we investigate the relationship between the MPD and SG and their implications for improving the robustness of SNNs. Our theoretical analysis reveals that reducing the proportion of membrane potentials lying within the gradient-available range of the SG function effectively mitigates the sensitivity of SNNs to input perturbations. Building upon this insight, we propose a novel MPD-driven surrogate gradient regularization (MPD-SGR) method, which enhances robustness by explicitly regularizing the MPD based on its interaction with the SG function. Extensive experiments across multiple image classification benchmarks and diverse network architectures confirm that the MPD-SGR method significantly enhances the resilience of SNNs to adversarial perturbations and exhibits strong generalizability across diverse network configurations, SG functions, and spike encoding schemes.

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IJCAI Conference 2025 Conference Paper

Adaptive Gradient Learning for Spiking Neural Networks by Exploiting Membrane Potential Dynamics

Jiaqiang Jiang
Lei Wang
Runhao Jiang
Jing Fan
Rui Yan

Recent advancements have focused on directly training high-performance spiking neural networks (SNNs) by estimating the approximate gradients of spiking activity through a continuous function with constant sharpness, known as surrogate gradient (SG) learning. However, as spikes propagate within neurons and among layers, the distribution of membrane potential dynamics (MPD) will deviate from the gradient-available interval of fixed SG, hindering SNNs from searching the optimal solution space. To maintain the stability of gradient flows, SG needs to align with evolving MPD. Here, we propose a novel adaptive gradient learning for SNNs by exploiting MPD, namely MPD-AGL. It fully accounts for the underlying factors contributing to membrane potential shifts and establishes a dynamic association between SG and MPD at different timesteps to relax gradient estimation, which provides a new degree of freedom for SG learning. Experimental results demonstrate that our method achieves excellent performance at low latency. Moreover, it increases the proportion of neurons that fall into the gradient-available interval compared to fixed SG, effectively mitigating the gradient vanishing problem. Code is available at https: //github. com/jqjiang1999/MPD-AGL.

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ICRA Conference 2025 Conference Paper

Brain-Inspired Spatial Continuous State Encoding for Efficient Spiking-Based Navigation

Qingao Chai
Jiashuo Wang
Runhao Jiang
Bo Yang
Rui Yan
Huajin Tang

Spiking neural networks (SNNs) show great potential in mapless navigation tasks due to their low power consumption, but the continuous representation of spatial information poses a challenge to SNN training. Neuroscience findings reveal that spatial cognition cells encode spatial information through population spike patterns. Inspired by this, we propose a navigation method based on SNNs, leveraging spatial cognition cells, which include grid cells (GCs), head direction cells (HDCs), and boundary vector cells (BVCs). Our method integrates spike-based information to achieve precise navigation goal encoding and egocentric environment perception, significantly improving SNN navigation capabilities in complex environments. Simulation and real-world experiments demonstrate that our method achieves significant improvements in navigation success rate and energy efficiency, showcasing superior adaptability across environments. Our work provides a novel approach to developing efficient brain-inspired navigation systems.

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AAAI Conference 2025 Conference Paper

GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL

Lang Qin
Ziming Wang
Runhao Jiang
Rui Yan
Huajin Tang

Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can significantly reduce the computational resource requirements for agents and improve the algorithm's performance under resource-constrained conditions. However, in current spiking reinforcement learning (SRL) algorithms, the simulation results of multiple time steps can only correspond to a single-step decision in RL. This is quite different from the real temporal dynamics in the brain and also fails to fully exploit the capacity of SNNs to process temporal data. In order to address this temporal mismatch issue and further take advantage of the inherent temporal dynamics of spiking neurons, we propose a novel temporal alignment paradigm (TAP) that leverages the single-step update of spiking neurons to accumulate historical state information in RL and introduces gated units to enhance the memory capacity of spiking neurons. Experimental results show that our method can solve partially observable Markov decision processes (POMDPs) and multi-agent cooperation problems with similar performance as recurrent neural networks (RNNs) but with about 50\% power consumption.

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ICML Conference 2023 Conference Paper

Adaptive Smoothing Gradient Learning for Spiking Neural Networks

Ziming Wang
Runhao Jiang
Shuang Lian
Rui Yan 0005
Huajin Tang

Spiking neural networks (SNNs) with biologically inspired spatio-temporal dynamics demonstrate superior energy efficiency on neuromorphic architectures. Error backpropagation in SNNs is prohibited by the all-or-none nature of spikes. The existing solution circumvents this problem by a relaxation on the gradient calculation using a continuous function with a constant relaxation de- gree, so-called surrogate gradient learning. Nevertheless, such a solution introduces additional smoothing error on spike firing which leads to the gradients being estimated inaccurately. Thus, how to adaptively adjust the relaxation degree and eliminate smoothing error progressively is crucial. Here, we propose a methodology such that training a prototype neural network will evolve into training an SNN gradually by fusing the learnable relaxation degree into the network with random spike noise. In this way, the network learns adaptively the accurate gradients of loss landscape in SNNs. The theoretical analysis further shows optimization on such a noisy network could be evolved into optimization on the embedded SNN with shared weights progressively. Moreover, The experiments on static images, dynamic event streams, speech, and instrumental sounds show the proposed method achieves state-of-the-art performance across all the datasets with remarkable robustness on different relaxation degrees.

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NeurIPS Conference 2023 Conference Paper

Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes

Gehua Ma
Runhao Jiang
Rui Yan
Huajin Tang

Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an unrealistic and inflexible processing paradigm. Meanwhile, these methods target trial-averaged firing rates and fail to capture important features in spike trains. This work presents the temporal conditioning spiking latent variable models ( TeCoS-LVM ) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains. This approach helps to avoid losing information embedded in the original spike trains. We exclude the temporal dimension from the model parameter space and introduce a temporal conditioning operation to allow the model to adaptively explore and exploit temporal dependencies in stimuli sequences in a natural paradigm. We show that TeCoS-LVM models can produce more realistic spike activities and accurately fit spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM models can generalize well to longer time scales. Overall, while remaining computationally tractable, our model effectively captures key features of neural coding systems. It thus provides a useful tool for building accurate predictive computational accounts for various sensory perception circuits.

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