文献类型：期刊文献

英文题名：Path integration based on a recurrent spiking neural network

作者：Lin, Jiaxin[1];Wang, Yihong[1,2];Xu, Xuying[1,2];Pan, Xiaochuan[1,2];Wang, Rubin[2,3]

机构：[1]East China Univ Sci & Technol, Inst Cognit Neurodynam, Sch Math, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Ctr Intelligent Comp, Sch Math, Shanghai 200237, Peoples R China;[3]Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China

年份：2026

卷号：34

期号：1

起止页码：1

外文期刊名：ELECTRONIC RESEARCH ARCHIVE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001650907600001)】；

基金：This study is supported by the National Natural Science Foundation of China (Grant Nos. 12172132, 12272136 and 12472054) and Science and Technology Commission of Shanghai Municipality (No. 24JS2810400) .

语种：英文

外文关键词：path integration; recurrent spiking neural network; spatial representations; locomotor representations; neurodynamics

摘要：Path integration refers to the process by which animals continuously update spatial position during movement by integrating locomotor stimuli such as speed and head direction, thereby enabling real-time localization in continuous space. This process relies on the cooperation of spatial neurons, including grid cells and place cells. In recent years, continuous attractor networks and recurrent neural networks have provided useful insights into the mechanisms of path integration; however, they often rely on fixed-weight connections or exhibit a lack of biological plausibility. To explore more biologically plausible mechanisms, we propose a path integrator based on the recurrent spiking neural network (RSNN). Employing recurrently connected leaky integrate and fire (LIF) neurons, the RSNN encodes spatial positions as spike sequences via membrane potential reset mechanisms, enabling robust long-term path integration. Analysis reveals the spontaneous emergence of spatial and locomotor units, with some units exhibiting spatial-locomotor conjunctive properties, indicating synergistic computations underlying path integration. Ablation experiments confirm that stripe and border units have a larger effect on performance than other unit types under our experimental conditions. Under sparse spiking conditions, the network naturally develops diverse biologically inspired representations. The RSNN's performance provides novel insights into neuronal synergistic mechanisms in biological navigation, offering a biologically grounded framework for path integration modeling and contributing to the development of brain-inspired navigation algorithms.