{"title":"鸽子视顶盖神经元信号的高质量RGB图像解码。","authors":"Zhen Dong, Yingjie Xiang, Songwei Wang","doi":"10.1016/j.jneumeth.2025.110595","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.</div></div><div><h3>New method</h3><div>We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.</div></div><div><h3>Results</h3><div>Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94 dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.</div></div><div><h3>Comparison with existing methods</h3><div>In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65 %, the structural similarity index (SSIM) increased by 38.92 %, the peak signal-to-noise ratio (PSNR) increased by 12.65 %, and the feature similarity index (FSIMc) increased by 9.28 %.</div></div><div><h3>Conclusions</h3><div>This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110595"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High - quality decoding of RGB images from the neuronal signals of the pigeon optic tectum\",\"authors\":\"Zhen Dong, Yingjie Xiang, Songwei Wang\",\"doi\":\"10.1016/j.jneumeth.2025.110595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.</div></div><div><h3>New method</h3><div>We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.</div></div><div><h3>Results</h3><div>Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94 dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.</div></div><div><h3>Comparison with existing methods</h3><div>In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65 %, the structural similarity index (SSIM) increased by 38.92 %, the peak signal-to-noise ratio (PSNR) increased by 12.65 %, and the feature similarity index (FSIMc) increased by 9.28 %.</div></div><div><h3>Conclusions</h3><div>This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.</div></div>\",\"PeriodicalId\":16415,\"journal\":{\"name\":\"Journal of Neuroscience Methods\",\"volume\":\"424 \",\"pages\":\"Article 110595\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuroscience Methods\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165027025002390\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroscience Methods","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165027025002390","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
High - quality decoding of RGB images from the neuronal signals of the pigeon optic tectum
Background
Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.
New method
We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.
Results
Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94 dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.
Comparison with existing methods
In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65 %, the structural similarity index (SSIM) increased by 38.92 %, the peak signal-to-noise ratio (PSNR) increased by 12.65 %, and the feature similarity index (FSIMc) increased by 9.28 %.
Conclusions
This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.
期刊介绍:
The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.