Biomimetic Visual Information Spatiotemporal Encoding Method for In Vitro Biological Neural Networks.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Xingchen Wang, Bo Lv, Fengzhen Tang, Yukai Wang, Bin Liu, Lianqing Liu
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Abstract

The integration of in vitro biological neural networks (BNNs) with robotic systems to explore their information processing and adaptive learning in practical tasks has gained significant attention in the fields of neuroscience and robotics. However, existing BNN-based robotic systems cannot perceive the visual environment due to the inefficiency of sensory information encoding methods. In this study, we propose a biomimetic visual information spatiotemporal encoding method based on improved delayed phase encoding. This method transforms high-dimensional images into a series of pulse sequences through convolution, temporal delay, alignment, and compression for BNN stimuli. We conduct three stages of unsupervised training on in vitro BNNs using high-density microelectrode arrays (HD-MEAs) to validate the potential of the proposed encoding method for image recognition tasks. The neural activity is decoded via a logistic regression model. The experimental results show that the firing patterns of BNNs with different spatiotemporal stimuli are highly separable in the feature space. After the third training stage, the image recognition accuracy reaches 80.33% ± 7.94%, which is 13.64% higher than that of the first training stage. Meanwhile, the BNNs exhibit significant increases in the connection number, connection strength, and inter-module participation coefficient after unsupervised training. These results demonstrate that the proposed method significantly enhances the functional connectivity and cross-module information exchange in BNNs.

体外生物神经网络仿生视觉信息时空编码方法。
将体外生物神经网络(BNNs)与机器人系统相结合,探索其在实际任务中的信息处理和自适应学习,已经引起了神经科学和机器人领域的广泛关注。然而,由于感官信息编码方法的低效,现有的基于神经网络的机器人系统无法感知视觉环境。在这项研究中,我们提出了一种基于改进延迟相位编码的仿生视觉信息时空编码方法。该方法将高维图像通过卷积、时间延迟、对齐和BNN刺激压缩,转化为一系列脉冲序列。我们使用高密度微电极阵列(hd - mea)对体外bnn进行了三个阶段的无监督训练,以验证所提出的编码方法在图像识别任务中的潜力。神经活动通过逻辑回归模型解码。实验结果表明,不同时空刺激下脑神经网络的放电模式在特征空间上具有高度可分离性。经过第三阶段的训练,图像识别准确率达到80.33%±7.94%,比第一阶段提高了13.64%。同时,经过无监督训练后,bnn的连接数、连接强度和模块间参与系数均显著增加。结果表明,该方法显著增强了生物神经网络的功能连通性和跨模块信息交换。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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