{"ID":2862698,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26085","arxiv_id":"2509.26085","title":"A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing","abstract":"Event cameras are bio-inspired vision sensor that encode visual information with high dynamic range, high temporal resolution, and low latency.Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing framework inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous-coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariant event sequences as periodic signals and polarity=changing event sequences as chaotic signals. We then use continuous wavelet transforms to analyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is validated through integration with conventional classification networks, achieving state-of-the-art classification accuracy on the N-Caltech101 and N-CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-based object classification while significantly enhancing the generalization and stability of event representation. Our code is available in https://github.com/chenyu0193/ACDF.","short_abstract":"Event cameras are bio-inspired vision sensor that encode visual information with high dynamic range, high temporal resolution, and low latency.Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting th...","url_abs":"https://arxiv.org/abs/2509.26085","url_pdf":"https://arxiv.org/pdf/2509.26085v1","authors":"[\"Yu Chen\",\"Jing Lian\",\"Zhaofei Yu\",\"Jizhao Liu\",\"Jisheng Dang\",\"Gang Wang\"]","published":"2025-09-30T10:56:41Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":608928,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2862698,"paper_url":"https://arxiv.org/abs/2509.26085","paper_title":"A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing","repo_url":"https://github.com/chenyu0193/ACDF","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}