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J. Neur. Syst."],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p> Thermal and RGB images exhibit significant differences in information representation, especially in low-light or nighttime environments. Thermal images provide temperature information, complementing the RGB images by restoring details and contextual information. However, the spatial discrepancy between different modalities in RGB-Thermal (RGB-T) semantic segmentation tasks complicates the process of multimodal feature fusion, leading to a loss of spatial contextual information and limited model performance. This paper proposes a channel-space fusion nonlinear spiking neural P system model network (CSPM-SNPNet) to address these challenges. This paper designs a novel color-thermal image fusion module to effectively integrate features from both modalities. During decoding, a nonlinear spiking neural P system is introduced to enhance multi-channel information extraction through the convolution of spiking neural P systems (ConvSNP) operations, fully restoring features learned in the encoder. Experimental results on public datasets MFNet and PST900 demonstrate that CSPM-SNPNet significantly improves segmentation performance. Compared with the existing methods, CSPM-SNPNet achieves a 0.5% improvement in mIOU on MFNet and 1.8% on PST900, showcasing its effectiveness in complex scenes. <\/jats:p>","DOI":"10.1142\/s0129065725500388","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:07:48Z","timestamp":1745924868000},"source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear Spiking Neural Systems for Thermal Image Semantic Segmentation Networks"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2197-8027","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. 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