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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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Can Robots Get Some Human Rights? A Cross-Disciplinary Discussion. JOURNAL OF ROBOTICS 2021. [DOI: 10.1155/2021/5461703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
An autonomous household robot passed a self-awareness test in 2015, proving that the cognitive capabilities of robots are heading towards those of humans. While this is a milestone in AI, it raises questions about legal implications. If robots are progressively developing cognition, it is important to discuss whether they are entitled to justice pursuant to conventional notions of human rights. This paper offers a comprehensive discussion of this complex question through cross-disciplinary scholarly sources from computer science, ethics, and law. The computer science perspective dissects hardware and software of robots to unveil whether human behavior can be efficiently replicated. The ethics perspective utilizes insights from robot ethics scholars to help decide whether robots can act morally enough to be endowed with human rights. The legal perspective provides an in-depth discussion of human rights with an emphasis on eligibility. The article concludes with recommendations including open research issues.
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George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J, Mayr C. Plasticity and Adaptation in Neuromorphic Biohybrid Systems. iScience 2020; 23:101589. [PMID: 33083749 PMCID: PMC7554028 DOI: 10.1016/j.isci.2020.101589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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Abstract
In this article, a practical look is taken at some of the possible enhancements for humans through the use of implants, particularly into the brain or nervous system. Some cognitive enhancements may not turn out to be practically useful, whereas others may turn out to be mere steps on the way to the construction of superhumans. The emphasis here is the focus on enhancements that take such recipients beyond the human norm rather than any implantations employed merely for therapy. This is divided into what we know has already been tried and tested and what remains at this time as more speculative. Five examples from the author’s own experimentation are described. Each case is looked at in detail, from the inside, to give a unique personal experience. The premise is that humans are essentially their brains and that bodies serve as interfaces between brains and the environment. The possibility of building an Interplanetary Creature, having an intelligence and possibly a consciousness of its own, is also considered.
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5
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Frequency variation analysis in neuronal cultures for stimulus response characterization. Neural Comput Appl 2020. [DOI: 10.1007/s00521-018-3942-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Abstract
In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches.
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Shimizu M, Minzan K, Kawashima H, Miyasaka K, Umedachi T, Ogura T, Nakai J, Ohkura M, Hosoda K. Self-organizing cell tactile perception which depends on mechanical stimulus history. Adv Robot 2019. [DOI: 10.1080/01691864.2019.1590232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Masahiro Shimizu
- Department of Systems Innovation, Osaka University Graduate School of Engineering Science, Toyonaka, Japan
| | - Kosuke Minzan
- Department of Multimedia Engineering, Osaka University Graduate School of Information Science Technology, Suita, Japan
| | - Hiroki Kawashima
- Department of Systems Innovation, Osaka University Graduate School of Engineering Science, Toyonaka, Japan
| | - Kota Miyasaka
- Gene Expression Laboratory - Evans (GEL-E), Salk Institute for Biological studies, La Jolla, CA, USA
| | - Takuya Umedachi
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Toshihiko Ogura
- Department of Developmental Neurobiology, Institute of Development, Aging, and Cancer and Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Junichi Nakai
- Graduate School of Science and Engineering, Brain and Body System Science Institute, Saitama University, Saitama, Japan
| | - Masamichi Ohkura
- Graduate School of Science and Engineering, Brain and Body System Science Institute, Saitama University, Saitama, Japan
| | - Koh Hosoda
- Department of Systems Innovation, Osaka University Graduate School of Engineering Science, Toyonaka, Japan
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Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ADVANCES IN NEUROBIOLOGY 2019; 22:351-387. [DOI: 10.1007/978-3-030-11135-9_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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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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Li Y, Sun R, Wang Y, Li H, Zheng X. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment. PLoS One 2016; 11:e0165600. [PMID: 27806074 PMCID: PMC5091833 DOI: 10.1371/journal.pone.0165600] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 10/15/2016] [Indexed: 11/19/2022] Open
Abstract
We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.
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Affiliation(s)
- Yongcheng Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
- University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Rong Sun
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, P. R. China
| | - Yuechao Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
| | - Hongyi Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
| | - Xiongfei Zheng
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
- * E-mail:
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12
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Nasuto SJ, Hayashi Y. Anticipation: Beyond synthetic biology and cognitive robotics. Biosystems 2016; 148:22-31. [DOI: 10.1016/j.biosystems.2016.07.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 07/25/2016] [Accepted: 07/31/2016] [Indexed: 10/21/2022]
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Wu Z, Zheng N, Zhang S, Zheng X, Gao L, Su L. Maze learning by a hybrid brain-computer system. Sci Rep 2016; 6:31746. [PMID: 27619326 PMCID: PMC5020320 DOI: 10.1038/srep31746] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 07/26/2016] [Indexed: 11/09/2022] Open
Abstract
The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.
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Affiliation(s)
- Zhaohui Wu
- College of Computer Science and Technology, Zhejiang University, China
| | - Nenggan Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, China
| | - Shaowu Zhang
- Research School of Biology, the Australian National University, Australia
| | - Xiaoxiang Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, China.,Department of Biomedical Engineering, Zhejiang University, China
| | - Liqiang Gao
- College of Computer Science and Technology, Zhejiang University, China.,Qiushi Academy for Advanced Studies, Zhejiang University, China
| | - Lijuan Su
- College of Computer Science and Technology, Zhejiang University, China
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Li Y, Sun R, Zhang B, Wang Y, Li H. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence. PLoS One 2015; 10:e0127452. [PMID: 25992579 PMCID: PMC4437899 DOI: 10.1371/journal.pone.0127452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 04/15/2015] [Indexed: 11/17/2022] Open
Abstract
Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.
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Affiliation(s)
- Yongcheng Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
| | - Rong Sun
- Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui, P. R. China
| | - Bin Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui, P. R. China
| | - Yuechao Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
| | - Hongyi Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
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In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using multielectrode arrays. Neural Plast 2015; 2015:196195. [PMID: 25866681 PMCID: PMC4381683 DOI: 10.1155/2015/196195] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Accepted: 02/27/2015] [Indexed: 11/18/2022] Open
Abstract
Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments.
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Stevenson A. We Came Here to Remember: Using Participatory Sensory Ethnography to Explore Memory as Emplaced, Embodied Practice. QUALITATIVE RESEARCH IN PSYCHOLOGY 2014. [DOI: 10.1080/14780887.2014.908990] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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17
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Bio-machine Hybrid Technology: A Theoretical Assessment and Some Suggestions for Improved Future Design. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/s13347-013-0130-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Downes JH, Hammond MW, Xydas D, Spencer MC, Becerra VM, Warwick K, Whalley BJ, Nasuto SJ. Emergence of a small-world functional network in cultured neurons. PLoS Comput Biol 2012; 8:e1002522. [PMID: 22615555 PMCID: PMC3355061 DOI: 10.1371/journal.pcbi.1002522] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2011] [Accepted: 04/01/2012] [Indexed: 11/19/2022] Open
Abstract
The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks.
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Affiliation(s)
- Julia H Downes
- School of Systems Engineering, University of Reading, Whiteknights, Reading, Berkshire, UK.
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Soe AK, Nahavandi S, Khoshmanesh K. Neuroscience goes on a chip. Biosens Bioelectron 2012; 35:1-13. [DOI: 10.1016/j.bios.2012.02.012] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2011] [Revised: 02/02/2012] [Accepted: 02/06/2012] [Indexed: 01/09/2023]
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