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Xu S, Liu Y, Yang Y, Zhang K, Liang W, Xu Z, Wu Y, Luo J, Zhuang C, Cai X. Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays. MICROMACHINES 2023; 14:709. [PMID: 37420942 DOI: 10.3390/mi14040709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 07/09/2023]
Abstract
Recent years have witnessed a spurt of progress in the application of the encoding and decoding of neural activities to drug screening, diseases diagnosis, and brain-computer interactions. To overcome the constraints of the complexity of the brain and the ethical considerations of in vivo research, neural chip platforms integrating microfluidic devices and microelectrode arrays have been raised, which can not only customize growth paths for neurons in vitro but also monitor and modulate the specialized neural networks grown on chips. Therefore, this article reviews the developmental history of chip platforms integrating microfluidic devices and microelectrode arrays. First, we review the design and application of advanced microelectrode arrays and microfluidic devices. After, we introduce the fabrication process of neural chip platforms. Finally, we highlight the recent progress on this type of chip platform as a research tool in the field of brain science and neuroscience, focusing on neuropharmacology, neurological diseases, and simplified brain models. This is a detailed and comprehensive review of neural chip platforms. This work aims to fulfill the following three goals: (1) summarize the latest design patterns and fabrication schemes of such platforms, providing a reference for the development of other new platforms; (2) generalize several important applications of chip platforms in the field of neurology, which will attract the attention of scientists in the field; and (3) propose the developmental direction of neural chip platforms integrating microfluidic devices and microelectrode arrays.
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Affiliation(s)
- Shihong Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yan Yang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kui Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yirong Wu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinping Luo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengyu Zhuang
- Department of Orthopaedics, Rujing Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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Chen Z, Liang Q, Wei Z, Chen X, Shi Q, Yu Z, Sun T. An Overview of In Vitro Biological Neural Networks for Robot Intelligence. CYBORG AND BIONIC SYSTEMS 2023; 4:0001. [PMID: 37040493 PMCID: PMC10076061 DOI: 10.34133/cbsystems.0001] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/17/2022] [Indexed: 01/12/2023] Open
Abstract
In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc. This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence. In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots. Next, we separate the intelligent behaviors into 2 parts according to whether they rely solely on the computing capacity (computing capacity-dependent) or depend also on the network plasticity (network plasticity-dependent), which are then expounded respectively, with a focus on those related to the realization of robot intelligence. Finally, the development trends and challenges of the BNN-based neurorobotic systems are discussed.
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Affiliation(s)
- Zhe Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
| | - Qian Liang
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zihou Wei
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Xie Chen
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Qing Shi
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhiqiang Yu
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Tao Sun
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
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Webster-Wood VA, Akkus O, Gurkan UA, Chiel HJ, Quinn RD. Organismal Engineering: Towards a Robotic Taxonomic Key for Devices Using Organic Materials. Sci Robot 2017; 2:eaap9281. [PMID: 31360812 PMCID: PMC6663099 DOI: 10.1126/scirobotics.aap9281] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Can we create robots with the behavioral flexibility and robustness of animals? Engineers often use bio-inspiration to mimic animals. Recent advances in tissue engineering now allow the use of components from animals. By integrating organic and synthetic components, researchers are moving towards the development of engineered organisms whose structural framework, actuation, sensing, and control are partially or completely organic. This review discusses recent exciting work demonstrating how organic components can be used for all facets of robot development. Based on this analysis, we propose a Robotic Taxonomic Key to guide the field towards a unified lexicon for device description.
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Affiliation(s)
| | - Ozan Akkus
- Dept. of Mech. and Aero. Engineering, Case Western Reserve University, Cleveland, OH, USA
- Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Dept. of Orthopaedics, Case Western Reserve University, Cleveland, OH, USA
| | - Umut A. Gurkan
- Dept. of Mech. and Aero. Engineering, Case Western Reserve University, Cleveland, OH, USA
- Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Dept. of Orthopaedics, Case Western Reserve University, Cleveland, OH, USA
| | - Hillel J. Chiel
- Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Dept. of Biology, Case Western Reserve University, Cleveland, OH, USA
- Dept. of Neurosciences, Case Western Reserve University, Cleveland, OH, USA
| | - Roger D. Quinn
- Dept. of Mech. and Aero. Engineering, Case Western Reserve University, Cleveland, OH, USA
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