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Dong S, Liu H, Wang Y, Bian J, Su J. Ferroelectricity-Defects Synergistic Artificial Synapses for High Recognition Accuracy Neuromorphic Computing. ACS Appl Mater Interfaces 2024; 16:19235-19246. [PMID: 38584351 DOI: 10.1021/acsami.4c01489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The ability of ferroelectric memristors to modulate conductance and offer multilevel storage has garnered significant attention in the realm of artificial synapses. On one hand, the resistance change of ferroelectric memristors mainly depends on the polarization reversal. On the other hand, the defects such as oxygen vacancies, which are inevitable presence during high-temperature processes, can undergo diffusion drift with the polarization reversal, thereby change the interface potential barrier. Thus, it is both desirable and necessary to investigate the synergistic effect of ferroelectricity and defects. Here, we prepare BaTiO3 ferroelectric memristor by pulse laser deposition and achieve resistance switching through the synergistic effect of ferroelectricity and oxygen vacancies. The memristor shows excellent switching characteristics with a large switching ratio (104) and good stability (103 s). It effectively emulates the features of artificial synapses and accomplishes decimal logical neural computing. In the neuromorphic system crafted with the memristor, the recognition accuracy of the 28 × 28 pixel image reaches 94.9%. These findings strongly support the research of ferroelectric memristors in neuromorphic devices.
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Affiliation(s)
- Shijie Dong
- College of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
| | - Hao Liu
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Yan Wang
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Jing Bian
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Jie Su
- College of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
- National Laboratory of Solid State Microstructures, Physics Department, Nanjing University, Nanjing 210093, People's Republic of China
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Zhang X, Dou Z, Kim SH, Upadhyay G, Havert D, Kang S, Kazemi K, Huang K, Aydin O, Huang R, Rahman S, Ellis‐Mohr A, Noblet HA, Lim KH, Chung HJ, Gritton HJ, Saif MTA, Kong HJ, Beggs JM, Gazzola M. Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons. Adv Sci (Weinh) 2024; 11:e2306826. [PMID: 38161217 PMCID: PMC10953569 DOI: 10.1002/advs.202306826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/12/2023] [Indexed: 01/03/2024]
Abstract
Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.
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Affiliation(s)
- Xiaotian Zhang
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Zhi Dou
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Seung Hyun Kim
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Gaurav Upadhyay
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Daniel Havert
- Department of PhysicsIndiana University BloomingtonBloomingtonIN47405USA
| | - Sehong Kang
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Kimia Kazemi
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Kai‐Yu Huang
- Department of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Onur Aydin
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Raymond Huang
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Saeedur Rahman
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Austin Ellis‐Mohr
- Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Hayden A. Noblet
- Molecular and Integrative PhysiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Neuroscience ProgramUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Ki H. Lim
- Molecular and Integrative PhysiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Hee Jung Chung
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Molecular and Integrative PhysiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Neuroscience ProgramUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Howard J. Gritton
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Comparative BiosciencesUniversity of Illinois at Urbana–ChampaignUrbanaIL61802USA
| | - M. Taher A. Saif
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Hyun Joon Kong
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - John M. Beggs
- Department of PhysicsIndiana University BloomingtonBloomingtonIN47405USA
| | - Mattia Gazzola
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
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Xu F, Ming D, Jung TP, Xu P, Xu M. Editorial: The application of artificial intelligence in brain-computer interface and neural system rehabilitation. Front Neurosci 2023; 17:1290961. [PMID: 37965214 PMCID: PMC10641882 DOI: 10.3389/fnins.2023.1290961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 10/05/2023] [Indexed: 11/16/2023] Open
Affiliation(s)
| | | | - Tzyy-Ping Jung
- University of California, San Diego, San Diego, CA, United States
| | - Peng Xu
- University of Electronic Science and Technology of China, Chengdu, China
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Végh J, Berki ÁJ. Towards Generalizing the Information Theory for Neural Communication. Entropy (Basel) 2022; 24:e24081086. [PMID: 36010750 PMCID: PMC9407630 DOI: 10.3390/e24081086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 05/06/2023]
Abstract
Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.
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Affiliation(s)
- János Végh
- Kalimános BT, 4028 Debrecen, Hungary
- Correspondence:
| | - Ádám József Berki
- Department of Neurology, Semmelweis University, 1085 Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, 1085 Budapest, Hungary
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Rundo F, Banna GL, Spampinato C, Conoci S. Editorial: Bio-Inspired Physiological Signal(s) and Medical Image(s) Neural Processing Systems Based on Deep Learning and Mathematical Modeling for Implementing Bio-Engineering Applications in Medical and Industrial Fields. Front Neuroinform 2021; 15:763699. [PMID: 34776917 PMCID: PMC8586079 DOI: 10.3389/fninf.2021.763699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/27/2021] [Indexed: 12/03/2022] Open
Affiliation(s)
| | | | | | - Sabrina Conoci
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
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Ali W, Li Y, Raja MAZ, Khan WU, He Y. State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing. Entropy (Basel) 2021; 23:e23091124. [PMID: 34573749 PMCID: PMC8471294 DOI: 10.3390/e23091124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/13/2021] [Accepted: 06/17/2021] [Indexed: 11/16/2022]
Abstract
In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.
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Affiliation(s)
- Wasiq Ali
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China; (W.A.); (Y.L.)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Yaan Li
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China; (W.A.); (Y.L.)
| | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Yunlin 64002, Taiwan;
| | - Wasim Ullah Khan
- School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
- Correspondence: (W.U.K.); (Y.H.)
| | - Yigang He
- School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
- Correspondence: (W.U.K.); (Y.H.)
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Li M, Tsien JZ. Neural Code- Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability. Front Cell Neurosci 2017; 11:236. [PMID: 28912685 PMCID: PMC5582596 DOI: 10.3389/fncel.2017.00236] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 07/25/2017] [Indexed: 12/05/2022] Open
Abstract
A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.
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Affiliation(s)
- Meng Li
- Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta UniversityAugusta, GA, United States
- The Brain Decoding Center, BanNa Biomedical Research Institute, Yunnan Academy of Science and TechnologyYunnan Province, China
| | - Joe Z. Tsien
- Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta UniversityAugusta, GA, United States
- The Brain Decoding Center, BanNa Biomedical Research Institute, Yunnan Academy of Science and TechnologyYunnan Province, China
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