材料科学
卡尔曼滤波器
解码方法
纤维
计算机科学
原发性震颤
外侧膝状核
神经科学
核心
桥(图论)
数码产品
编码(内存)
背
导电体
滤波器(信号处理)
人工智能
信号(编程语言)
极限(数学)
光纤
脑深部刺激
跟踪(教育)
生物医学工程
神经影像学
计算机视觉
作者
Liyuan Wang,Chengqiang Tang,Zhengqi Han,Haixin Zhong,K Zhang,Ziyi Xie,Hang Guan,Peng Zhai,Hui Li,Jiaheng Liang,Yi-Xiang Wang,Jiawei Chen,Yiqing Yang,Liu Z,Mingyi Huang,Sihui Yu,Qingquan Han,Xiangran Cheng,Jinyan Li,Jiahao Shen
标识
DOI:10.1002/adma.202519697
摘要
Fiber electronics provide the most promising platform for the detection, modulation, and reconstruction of biosignals in the brain. However, preserving stable communication between fiber electronics and cellular-scale targets in the deep brain is critical but challenging because of their mechanical mismatch. Here, our study fills this gap by developing a radial modulus-gradient fiber (RMGF), which can bridge high-modulus conductive components (MPa) and low-modulus brain tissue (kPa) to well eliminate the mechanical mismatch at the entire neural‒device interface. The RMGF exhibits strain-insensitive electrical properties (<0.2% resistance fluctuation over 700,000 stretching‒release cycles). As an example, the RMGF enables unprecedented five-month continuous tracking of single neurons in the dorsal lateral geniculate nucleus of freely moving cats, and allows reconstruction of visual stimuli with the use of only three neurons, with a high correlation coefficient of 0.95, approaching the theoretical limit of the unscented Kalman filter (0.97). The results indicate that dorsal lateral geniculate nucleus neurons maintain stable tuning properties (spatial frequency sensitivity, ON/OFF characteristics, and X-cell classification) and reveal a minimal effective ensemble for efficient encoding of information within deep thalamic circuits. This RMGF represents a platform for chronic recording at the single-cell level and investigating fundamental mechanisms in the deep tissues.
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