鸽子视顶盖神经元信号的高质量RGB图像解码。

IF 2.3 4区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Zhen Dong, Yingjie Xiang, Songwei Wang
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引用次数: 0

摘要

背景:解码神经活动以对感官输入进行逆向工程,推进了对神经编码的理解,并促进了脑机接口和视觉假体技术的发展。一个主要的挑战是从自然场景中重建高质量的RGB图像,本研究利用鸽子的视觉顶盖神经元来解决这个问题。新方法:通过微电极阵列捕获顶盖神经元对RGB图像的ON-OFF响应,建立神经响应数据集。一种集成了卷积编码网络、线性解码器和图像增强网络的模块化解码算法,实现了神经信号的逆RGB图像重建。结果:实验结果证实了该算法重构出高质量的RGB图像。所有测试集重构的平均指标为:相关系数(R)为0.853,结构相似度指数(SSIM)为0.618,峰值信噪比(PSNR)为19.94dB,特征相似度指数(FSIMc)为0.801。这些结果证实了原始图像的颜色和轮廓细节的准确再现。与现有方法的比较:在关键量化指标上,本文算法较传统线性重建方法有了显著改进,相关系数(R)提高了12.65%,结构相似度指数(SSIM)提高了38.92%,峰值信噪比(PSNR)提高了12.65%,特征相似度指数(FSIMc)提高了9.28%。结论:本研究为高质量的视觉神经解码提供了一种新的技术途径,并通过稳健的实验指标验证了其有效性。这也为研究鸟类视觉通路的信息处理机制提供了实验证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Journal of Neuroscience Methods
Journal of Neuroscience Methods 医学-神经科学
CiteScore
7.10
自引率
3.30%
发文量
226
审稿时长
52 days
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.
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