Artificial sensory system based on memristive devices

被引:16
作者
Kwon, Ju Young [1 ]
Kim, Ji Eun [1 ,2 ]
Kim, Jong Sung [1 ,2 ]
Chun, Suk Yeop [1 ,3 ]
Soh, Keunho [1 ,2 ]
Yoon, Jung Ho [1 ]
机构
[1] Korea Inst Sci & Technol KIST, Elect Mat Res Ctr, Seoul 02791, South Korea
[2] Korea Univ, Dept Mat Sci & Engn, Seoul, South Korea
[3] Korea Univ, KU KIST Grad Sch Converging Sci & Technol, Seoul, South Korea
来源
EXPLORATION | 2024年 / 4卷 / 01期
基金
新加坡国家研究基金会;
关键词
artificial neuron; artificial receptor; artificial sensory system; artificial synapse; memristor; TIMING-DEPENDENT PLASTICITY; LONG-TERM PLASTICITY; NEURAL-NETWORK; SYNAPTIC PLASTICITY; SWITCHING CHARACTERISTICS; VOLATILE MEMRISTOR; THIN-FILM; MEMORY; MECHANISMS; NEURONS;
D O I
10.1002/EXP.20220162
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed. The memristor-based artificial sensory systems composed of integrated and cooperative parallel networks of artificial sensory receptors, neurons, and synapses are reviewed. Specific mechanisms and critical functions required by the memristive device are reviewed. The reported studies that functionally implement artificial sensory systems with memristive devices are introduced, discussing the limitations and future research directions in developing memristor-based artificial sensory systems. image
引用
收藏
页数:35
相关论文
共 323 条
  • [1] Abbott T. B., 1990, STAT MECH NEURAL NET
  • [2] The Sensory Neurons of Touch
    Abraira, Victoria E.
    Ginty, David D.
    [J]. NEURON, 2013, 79 (04) : 618 - 639
  • [3] Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research
    Agatonovic-Kustrin, S
    Beresford, R
    [J]. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, 2000, 22 (05) : 717 - 727
  • [4] Memristor variability and stochastic physical properties modeling from a multivariate time series approach
    Alonso, F. J.
    Maldonado, D.
    Aguilera, A. M.
    Roldan, J. B.
    [J]. CHAOS SOLITONS & FRACTALS, 2021, 143
  • [5] Equivalent-accuracy accelerated neural-network training using analogue memory
    Ambrogio, Stefano
    Narayanan, Pritish
    Tsai, Hsinyu
    Shelby, Robert M.
    Boybat, Irem
    di Nolfo, Carmelo
    Sidler, Severin
    Giordano, Massimo
    Bodini, Martina
    Farinha, Nathan C. P.
    Killeen, Benjamin
    Cheng, Christina
    Jaoudi, Yassine
    Burr, Geoffrey W.
    [J]. NATURE, 2018, 558 (7708) : 60 - +
  • [6] Impact of the Mechanical Stress on Switching Characteristics of Electrochemical Resistive Memory
    Ambrogio, Stefano
    Balatti, Simone
    Choi, Seol
    Ielmini, Daniele
    [J]. ADVANCED MATERIALS, 2014, 26 (23) : 3885 - 3892
  • [7] Nanosecond threshold switching of GeTe6 cells and their potential as selector devices
    Anbarasu, M.
    Wimmer, Martin
    Bruns, Gunnar
    Salinga, Martin
    Wuttig, Matthias
    [J]. APPLIED PHYSICS LETTERS, 2012, 100 (14)
  • [8] MODEL FOR ELECTRONIC-STRUCTURE OF AMORPHOUS-SEMICONDUCTORS
    ANDERSON, PW
    [J]. PHYSICAL REVIEW LETTERS, 1975, 34 (15) : 953 - 955
  • [9] Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits
    Andrade-Talavera, Yuniesky
    Fisahn, Andre
    Rodriguez-Moreno, Antonio
    [J]. MOLECULAR PSYCHIATRY, 2023, 28 (06) : 2177 - 2188
  • [10] Training multi-layer spiking neural networks using NormAD based spatio-temporal error backpropagation
    Anwani, Navin
    Rajendran, Bipin
    [J]. NEUROCOMPUTING, 2020, 380 : 67 - 77