文献类型：期刊文献

英文题名：EMERGENT ORIENTATION MAPS - MECHANISMS, CODING EFFICIENCY AND ROBUSTNESS

作者：Zhong, Haixin[1,2]; Wang, Haoyu[3]; Dai, Wei P.[1,4]; Huang, Yuchao[5,6]; Huang, Mingyi[1,3]; Wang, Rubin[7]; Roe, Anna Wang[8]; Yu, Yuguo[1,2,3,4]

机构：[1] Research Institute of Intelligent Complex Systems, Fudan University, China; [2] State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, China; [3] Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China; [4] Shanghai Artificial Intelligence Laboratory, China; [5] IDG/McGovern Institute for Brain Research, School of Medicine, Tsinghua University, China; [6] Tsinghua-Peking Joint Center for Life Sciences, China; [7] Institute for Cognitive Neurodynamics, East China University of Science and Technology, China; [8] MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Key Laboratory of Biomedical Engineering, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, China

年份：2025

起止页码：75824

外文期刊名：13th International Conference on Learning Representations, ICLR 2025

收录：EI(收录号：20252818765275)

语种：英文

摘要：Extensive experimental studies have shown that in lower mammals, neuronal orientation preference in the primary visual cortex is organized in disordered "salt- and-pepper" organizations. In contrast, higher-order mammals display a continuous variation in orientation preference, forming pinwheel-like structures. Despite these observations, the spiking mechanisms underlying the emergence of these distinct topological structures and their functional roles in visual processing remain poorly understood. To address this, we developed a self-evolving spiking neural network model with Hebbian plasticity, trained using physiological parameters characteristic of rodents, cats, and primates, including retinotopy, neuronal morphology, and connectivity patterns. Our results identify critical factors, such as the degree of input visual field overlap, neuronal connection range, and the balance between localized connectivity and long-range competition, that determine the emergence of either salt-and-pepper or pinwheel-like topologies. Furthermore, we demonstrate that pinwheel structures exhibit lower wiring costs and enhanced sparse coding capabilities compared to salt-and-pepper organizations. They also maintain greater coding robustness against noise in naturalistic visual stimuli. These findings suggest that such topological structures confer significant computational advantages in visual processing and highlight their potential application in the design of brain-inspired deep learning networks and algorithms. ? 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.