Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Direct training of SNNs typically relies on surrogate gradient (SG) learning to estimate derivatives of non-differentiable spiking activity. However, during training, the distribution of neuronal membrane potentials varies across timesteps and progressively deviates toward both sides of the firing threshold. When the firing threshold and SG remain fixed, this may lead to imbalanced spike firing and diminished gradient signals, preventing SNNs from performing well. To address these issues, we propose a novel dual-stage synergistic learning algorithm that achieves forward adaptive thresholding and backward dynamic SG. In forward propagation, we adaptively adjust thresholds based on the distribution of membrane potential dynamics (MPD) at each timestep, which enriches neuronal diversity and effectively balances firing rates across timesteps and layers. In backward propagation, drawing from the underlying association between MPD, threshold, and SG, we dynamically optimize SG to enhance gradient estimation through spatio-temporal alignment, effectively mitigating gradient information loss. Experimental results demonstrate that our method achieves significant performance improvements. Moreover, it allows neurons to fire stable proportions of spikes at each timestep and increases the proportion of neurons that obtain gradients in deeper layers.

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.

Video surveillance is widely used in various public spaces, and person re-identification (ReId) based on video frames has become a research hotspot in the field of computer vision. However, most video-based methods struggle with issues such as poor lighting or motion blur, which result in blurry textures and hinder the extraction of discriminative features for specific identities. Additionally, frame-based methods may introduce privacy concerns and suffer from redundant storage between frames. In contrast, bio-inspired sensors—such as event cameras—have asynchronous properties and higher temporal resolution. They record events when changes in light intensity occur, offering a better response to lighting variations in specific scenes. Spiking Neural Networks (SNNs), which transmit information through sparse spikes, are naturally suited to process such asynchronous and sparse event-stream inputs. In this work, we explore the possibility of using only SNN for person ReId based on event-stream data generated by event cameras for the first time. While SNNs for person ReId suffer from issues such as blurry matching and poor feature distinction in spike-based feature matching, we propose an adaptive temporal feature representation (ATFR) method based on membrane potential to address these issues. We evaluate our method on two event-based datasets (Event-ReId and Event-PRID-2011). Compared to existing methods, our model achieves competitive performance while improving energy efficiency by at least 6 times.

Event-based cameras provide a unique way of visual perception through the event-driven characteristics and representation of sparse spatiotemporal data. Meanwhile, as brain-inspired models, Spiking Neural Networks (SNNs) have asynchronous event processing characteristics and are able to process event data naturally. However, various feature extraction methods currently in SNNs have not fully utilized the temporal information in the output of bionic visual sensors, making it difficult to better maintain Address Event Representations (AER). In this paper, we proposed a method to enhance time surfaces by extracting hidden temporal information in the time surface and multi-scale dilated time surface. The method of extracting hidden temporal information (EHTI) is to enhance the time surface by exploiting the information of time surface changes between the current event and other events in the spatiotemporal neighborhood, allowing the event’s temporal features to be fully utilized. In addition, we adopted the multi-scale dilated time surface (MDTS) method to solve the problem of limited event receptive fields by using dilated time surfaces for multi-scale time surface feature extraction, which can better capture the spatiotemporal connections between events in further neighborhoods. Experimental results on various event-based datasets (i.e., N-MNIST, MNIST-DVS, DVS128 Gesture, and DailyAction-DVS) show that our method is able to extract richer features and outperforms other methods on classification tasks.