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Li X, Zhong Y, Chen H, Tang J, Zheng X, Sun W, Li Y, Wu D, Gao B, Hu X, Qian H, Wu H. A Memristors-Based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203684. [PMID: 35735048 DOI: 10.1002/adma.202203684] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy-efficient neuromorphic computing. To this end, a titanium oxide (TiOx )-based interface-type dynamic memristor and an niobium oxide (NbOx )-based Mott memristor are integrated as an artificial dendrite and spike-firing soma, respectively, to construct a dendritic neuron unit for realizing high-efficiency spatial-temporal information processing. Further, a dendritic neural network is hardware-implemented for spatial-temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University-Red Green Blue (NTU-RGB) dataset as a benchmark spatial-temporal task; it shows a nearly 20% improvement in accuracy for the memristors-based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio-plausible neural networks that can manage complex spatial-temporal tasks with high efficiency.
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Affiliation(s)
- Xinyi Li
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Yanan Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Hang Chen
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Jianshi Tang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Xiaojian Zheng
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Wen Sun
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Yang Li
- Department of Internet of Things Technology and Application, China Mobile Research Institute, Beijing, 100053, China
| | - Dong Wu
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Bin Gao
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Xiaolin Hu
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - He Qian
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Huaqiang Wu
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
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Brazhe AR, Postnov DE, Sosnovtseva O. Astrocyte calcium signaling: Interplay between structural and dynamical patterns. CHAOS (WOODBURY, N.Y.) 2018; 28:106320. [PMID: 30384660 DOI: 10.1063/1.5037153] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 09/24/2018] [Indexed: 06/08/2023]
Abstract
Inspired by calcium activity in astrocytes, which is different in the cell body and thick branches on the one hand and thin branchlets and leaflets on the other hand, we formulate a concept of spatially partitioned oscillators. These are inhomogeneous media with regions having different excitability properties, with a global dynamics governed by spatial configuration of such regions. Due to a high surface-to-volume ratio, calcium dynamics in astrocytic leaflets is dominated by transmembrane currents, while somatic calcium dynamics relies on exchange with intracellular stores, mediated by IP 3 , which is in turn synthesized in the space nearby the plasma membrane. Reciprocal coupling via diffusion of calcium and IP 3 between the two regions makes the spatial configuration an essential contributor to overall dynamics. Due to these features, the mechanisms governing the pattern formation of calcium dynamics differ from classical excitable systems with noise or from networks of clustered oscillators. We show how geometrical inhomogeneity can play an ordering role allowing for stable scenarios for calcium wave initiation and propagation.
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Affiliation(s)
- A R Brazhe
- Biological Faculty, Lomonosov Moscow State University, Leninskie Gory 1/24, 119234 Moscow, Russia
| | - D E Postnov
- Department of Physics, Saratov State University, Astrakhanskaya st. 83, 410012 Saratov, Russia
| | - O Sosnovtseva
- Faculty of Health and Medical Sciences, Department of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen, Denmark
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Palmer JHC, Gong P. Formation and regulation of dynamic patterns in two-dimensional spiking neural circuits with spike-timing-dependent plasticity. Neural Comput 2013; 25:2833-57. [PMID: 24001345 DOI: 10.1162/neco_a_00511] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongoing patterns in the neural circuit and STDP. This interaction is analyzed by developing a simple model able to capture its essential dynamics, which give rise to symmetry breaking. This occurs in a fundamentally self-organizing manner, without fine-tuning of the system parameters. Moreover, we find that STDP provides a synaptic mechanism to learn the paths taken by spiking waves and modulate the dynamics of their interactions, enabling them to be regulated. This regulation mechanism has error-correcting properties. Our results therefore highlight the important roles played by STDP in facilitating the formation and regulation of spiking wave patterns that may have crucial functional roles in brain information processing.
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Affiliation(s)
- John H C Palmer
- School of Physics, University of Sydney, Sydney 2006, NSW, Australia
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