Learning Robust Event Representations through Finite-Time Dynamical Stability

Event cameras are a class of bio-inspired vision sensors with advantages such as high temporal resolution, low latency, and high dynamic range. Existing state-of-the-art event processing methods mainly rely on deep networks to learn features, but these methods do not consider the internal temporal dynamics of the model, causing models to learn fragile temporal features and thus suffer significant performance degradation under distribution shifts such as event dropout, temporal jitter, and polarity perturbations. To address this issue, incorporating recent findings in the primary visual cortex, we propose Lyapunov-Regularized Dynamical Representation Learning (LRDR) for event information processing. Specifically, this method maps event streams into a trainable nonlinear dynamical system and uses finite-time Lyapunov exponents to constrain the dynamical stability of different samples, encouraging the same sample to maintain consistency under perturbations while enhancing dynamical separability between different classes. Across four event-recognition benchmarks, LRDR achieves state-of-the-art classification accuracy of 85.7% and 99.9% on N-Caltech101 and ASL-DVS, respectively. Our results demonstrate that finite-time stability is not merely an auxiliary property for analyzing the dynamics of neural networks after training, but can be directly optimized as a learnable representational geometry, thereby providing more stable and robust temporal representations for event-based vision models.