Recurrent optical neural network for the study of pattern dynamics

被引:0
|
作者
Berger, C [1 ]
Collings, N [1 ]
Gehriger, D [1 ]
机构
[1] Univ Neuchatel, Inst Microtechnol, CH-2000 Neuchatel, Switzerland
来源
关键词
optical micro-system; optical neural network; optical feedback; LCTV; LCLV; microlens array; Dammann-grating; neural network dynamics; asynchronous feedback;
D O I
10.1117/12.304949
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We report on ongoing work with a compact all-optical recurrent neural network with 16 x 16 channels and 256 x 256 reconfigurable interconnects (weights). We will present the optical setup and report on experimental work with the system and its building blocks. The microlens-based setup shows excellent imaging properties and easy alignability. After optimizing the setup, losses could be reduced by more than an order of magnitude. The system performance is currently limited by inhomogeneities of the thresholding device.
引用
收藏
页码:233 / 244
页数:12
相关论文
共 50 条
  • [1] Robust Pattern Recognition Using Chaotic Dynamics in Attractor Recurrent Neural Network
    Azarpour, M.
    Seyyedsalehi, S. A.
    Taherkhani, A.
    [J]. 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010, 2010,
  • [2] Physics-informed recurrent neural network for time dynamics in optical resonances
    Yingheng Tang
    Jichao Fan
    Xinwei Li
    Jianzhu Ma
    Minghao Qi
    Cunxi Yu
    Weilu Gao
    [J]. Nature Computational Science, 2022, 2 : 169 - 178
  • [3] Physics-informed recurrent neural network for time dynamics in optical resonances
    Tang, Yingheng
    Fan, Jichao
    Li, Xinwei
    Ma, Jianzhu
    Qi, Minghao
    Yu, Cunxi
    Gao, Weilu
    [J]. NATURE COMPUTATIONAL SCIENCE, 2022, 2 (03): : 169 - 178
  • [4] Optical neural network for pattern analysis
    Pavlov, AV
    [J]. PROGRESS IN CONNECTIONIST-BASED INFORMATION SYSTEMS, VOLS 1 AND 2, 1998, : 733 - 736
  • [5] Data compression by recurrent neural network dynamics
    Li, LK
    [J]. 1996 IEEE TENCON - DIGITAL SIGNAL PROCESSING APPLICATIONS PROCEEDINGS, VOLS 1 AND 2, 1996, : 96 - 101
  • [6] Symbolic Representation of Recurrent Neural Network Dynamics
    Huynh, Thuan Q.
    Reggia, James A.
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2012, 23 (10) : 1649 - 1658
  • [7] On the macroscopic description of recurrent neural network dynamics
    Tanaka, T
    Osawa, S
    [J]. JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 1998, 31 (18): : 4197 - 4202
  • [8] Analyzing state dynamics in a recurrent neural network
    Spiegel, R
    Suret, M
    Le Pelley, ME
    McLaren, IPL
    [J]. PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 834 - 839
  • [9] Weather prediction by recurrent neural network dynamics
    Biswas, Saroj Kr.
    Sinha, Nidul
    Purkayastha, Biswajit
    Marbaniang, Leniency
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT ENGINEERING INFORMATICS, 2014, 2 (2-3) : 166 - 180
  • [10] Recurrent neural network as a linear attractor for pattern association
    Seow, MJ
    Asari, VK
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (01): : 246 - 250