MODELS OF CONTINUOUS NEURAL NETWORKS

被引:3
|
作者
CARMESIN, HO
机构
[1] Institute of Theoretical Physics, University of Bremen
关键词
D O I
10.1016/0375-9601(91)90934-Z
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
A neural network is investigated, in which the state of a neuron is described by any real number, while its mean quadratic activation is one. The Hopfield learning rule is assumed and generalized. The net is equivalent to one spin in a 2(2l)-polar external field, and its capacity is at most l(N), where N is the number of neurons. The infinite-polar net exhibits infinite capacity and both continuous and discontinuous phase transitions.
引用
收藏
页码:183 / 186
页数:4
相关论文
共 50 条
  • [1] Continuous Self-Attention Models with Neural ODE Networks
    Zhang, Jing
    Zhang, Peng
    Kong, Baiwen
    Wei, Junqiu
    Jiang, Xin
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 14393 - 14401
  • [2] DISCRETE-TIME VERSUS CONTINUOUS-TIME MODELS OF NEURAL NETWORKS
    WANG, X
    BLUM, EK
    JOURNAL OF COMPUTER AND SYSTEM SCIENCES, 1992, 45 (01) : 1 - 19
  • [3] Approximation by neural networks is not continuous
    Kainen, PC
    Kurková, V
    Vogt, A
    NEUROCOMPUTING, 1999, 29 (1-3) : 47 - 56
  • [4] CONTINUOUS UNLEARNING IN NEURAL NETWORKS
    YOUN, CH
    KAK, SC
    ELECTRONICS LETTERS, 1989, 25 (03) : 202 - 203
  • [5] Dynamics of neural networks with continuous attractors
    Fung, C. C. Alan
    Wong, K. Y. Michael
    Wu, Si
    EPL, 2008, 84 (01)
  • [6] Generalisation of a class of continuous neural networks
    ShaweTaylor, J
    Zhao, JY
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 8: PROCEEDINGS OF THE 1995 CONFERENCE, 1996, 8 : 267 - 273
  • [7] Continuous Safety Verification of Neural Networks
    Cheng, Chih-Hong
    Yan, Rongjie
    PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021), 2021, : 1478 - 1483
  • [8] Lipschitz continuous neural networks on Lp
    Fromion, V
    PROCEEDINGS OF THE 39TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 2000, : 3528 - 3533
  • [9] Models of the dynamics of neural networks
    Shubnikov, EI
    JOURNAL OF OPTICAL TECHNOLOGY, 1999, 66 (04) : 346 - 359
  • [10] PHYSICAL MODELS OF NEURAL NETWORKS
    LUKASHIN, AV
    VEDENOV, AA
    FRANKKAMENETSKII, MD
    BIOFIZIKA, 1987, 32 (05): : 918 - 928