Real Time Astrocyte in Spiking Neural Network

被引:0
|
作者
Abed, Bassam Abdul-Rahman [1 ]
Ismail, Amelia Ritahani [1 ]
Aziz, Normaziah Abdul [1 ]
机构
[1] Int Islamic Univ Malaysia, Dept Comp Sci, Kuala Lumpur, Malaysia
关键词
Spiking Response Model; Spiking Neural Network; Astrocytes; DRESSED NEURONS; MODEL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Astrocytes, one type of glial cells, are considered to be an active partner to neurons in processing information of Central Nervous System (CNS). Therefore, studying the interaction between the astrocytes and neurons is important to create a novel model for Artificial Neuron-Glial Networks (ANGN). In this paper, a novel model for (ANGN) is proposed to model the real time interaction between Astrocytes and neurons by using Spiking Neural Networks (SNNs) and mathematical models for astrocyte-neuron interaction. How could this proposed model will be biologically inspired to model the real time interaction between astrocytes and neurons and to improve the performance of the SNN? However, these mathematical models are generalized and simplified to be used in the proposed network. The performance of the proposed network was compared with standard SNN and the simulation results showed that the proposed model evoked more spikes to fire whenever astrocytes were activating in a time window. This indicates that astrocytes are playing significant roles in processing information of the ANGN.
引用
收藏
页码:565 / 570
页数:6
相关论文
共 50 条
  • [41] Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor
    Li, Ruixin
    Zhao, Guoxu
    Muir, Dylan Richard
    Ling, Yuya
    Burelo, Karla
    Khoe, Mina
    Wang, Dong
    Xing, Yannan
    Qiao, Ning
    Computers in Biology and Medicine, 2024, 183
  • [42] Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework
    Costa, Filippo
    Schaft, Eline V.
    Huiskamp, Geertjan
    Aarnoutse, Erik J.
    van't Klooster, Maryse A.
    Krayenbuhl, Niklaus
    Ramantani, Georgia
    Zijlmans, Maeike
    Indiveri, Giacomo
    Sarnthein, Johannes
    NATURE COMMUNICATIONS, 2024, 15 (01)
  • [43] Real-Time Correlation Detection via Online Learning of a Spiking Neural Network with a Conductive-Bridge Neuron
    Kim, Dong-Won
    Woo, Dae-Seong
    Kim, Hea-Jee
    Jin, Soo-Min
    Jung, Sung-Mok
    Kim, Dong-Eon
    Kim, Jae-Joon
    Shim, Tae-Hun
    Park, Jea-Gun
    ADVANCED ELECTRONIC MATERIALS, 2022, 8 (07):
  • [44] Photonics-enabled spiking timing-dependent convolutional neural network for real-time image classification
    Meng, Xiangyan
    Shi, Nuannuan
    Shi, Difei
    Li, Wei
    Li, Ming
    OPTICS EXPRESS, 2022, 30 (10) : 16217 - 16228
  • [45] A Real-time Silicon Cerebellum Spiking Neural Model based on FPGA
    Luo, Junwen
    Coapes, Graeme
    Degenaar, Patrick
    Mak, Terrence
    Yamazaki, Tadashi
    Tin, Chung
    2014 14TH INTERNATIONAL SYMPOSIUM ON INTEGRATED CIRCUITS (ISIC), 2014, : 276 - 279
  • [46] Breaking the virtual barrier: real-time interactions with spiking neural models
    Corey M Thibeault
    Frederick C Harris
    Philip H Goodman
    BMC Neuroscience, 11 (Suppl 1)
  • [47] A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks
    Kiggins, Jacob
    Schaffer, J. David
    Merkel, Cory
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [48] A real time learning neural network for real world applications
    Glass, EJ
    Reid, LJ
    SIXTH, SEVENTH, AND EIGHTH WORKSHOPS ON VIRTUAL INTELLIGENCE: ACADEMIC/INDUSTRIAL/NASA/DEFENSE TECHNICAL INTERCHANGE AND TUTORIALS, 1996, 2878 : 21 - 29
  • [49] Supervised learning in a spiking neural network
    Myoung Won Cho
    Journal of the Korean Physical Society, 2021, 79 : 328 - 335
  • [50] Backpropagation in Spiking Neural Network Using Reverse Spiking Mechanism
    Malathi, M.
    Faiyaz, K. K.
    Naveen, R. M.
    Nithish, C.
    THIRD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND CAPSULE NETWORKS (ICIPCN 2022), 2022, 514 : 507 - 518