神经形态工程学
人工神经网络
接口(物质)
神经活动
生物神经网络
材料科学
纳米技术
神经科学
脑-机接口
人工智能
计算机科学
生物
机器学习
气泡
脑电图
最大气泡压力法
并行计算
作者
Grace A. Woods,Nicholas J. Rommelfanger,Guosong Hong
出处
期刊:Matter
[Elsevier]
日期:2020-10-01
卷期号:3 (4): 1087-1113
被引量:42
标识
DOI:10.1016/j.matt.2020.08.002
摘要
Summary
The success of in vivo neural interfaces relies on their long-term stability and large scale in interrogating and manipulating neural activity after implantation. Conventional neural probes, owing to their limited spatiotemporal resolution and scale, face challenges for studying the massive, interconnected neural network in its native state. In this review, we argue that taking inspiration from biology will unlock the next generation of in vivo bioelectronic neural interfaces. Reducing the feature sizes of bioelectronic neural interfaces to mimic those of neurons enables high spatial resolution and multiplexity. Additionally, chronic stability at the device-tissue interface is realized by matching the mechanical properties of bioelectronic neural interfaces to those of the endogenous tissue. Furthermore, modeling the design of neural interfaces after the endogenous topology of the neural circuitry enables new insights into the connectivity and dynamics of the brain. Lastly, functionalization of neural probe surfaces with coatings inspired by biology leads to enhanced tissue acceptance over extended timescales. Bioinspired neural interfaces will facilitate future developments in neuroscience studies and neurological treatments by leveraging bidirectional information transfer and integrating neuromorphic computing elements.
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