Open questions for suprathreshold stochastic resonance in sensory neural models for motion detection using artificial insect vision

被引:0
|
作者
McDonnell, MD [1 ]
Abbott, D [1 ]
机构
[1] Univ Adelaide, Ctr Biomed Engn, CBME, Adelaide, SA 5005, Australia
来源
UNSOLVED PROBLEMS OF NOISE AND FLUCTUATIONS | 2003年 / 665卷
关键词
D O I
暂无
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Stochastic Resonance (SR) occurs when the presence of noise in a nonlinear system can induce an optimal output from that system, and has been observed in a diverse range of physical and biological systems, including neurons. Despite this widespread observation of SR, to date very few engineering applications inspired by SR have been proposed, and one of the goals of our research is to explore possible new practical applications designed to replicate the benefits of SR. In particular, since about 1991, our group has designed and implemented a number of motion detection VLSI chips based on insect vision. We are currently investigating the possibility of replicating the benefits of SR in artificial insect-vision based motion detection systems, in particular a newly described form of SR called Suprathreshold Stochastic Resonance (SSR). The current paper is intended to review and identify the key open questions and avenues for future research relating to SR and SSR in such systems.
引用
收藏
页码:51 / 58
页数:8
相关论文
共 45 条
  • [31] Stochastic ground motion models to NGA-West2 and NGA-Sub databases using Bayesian neural network
    Sreenath, Vemula
    Raghukanth, S. T. G.
    EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS, 2023, 52 (01): : 248 - 267
  • [32] EVALUATION OF DIFFERENT PEAK MODELS OF EYE BLINK EEG FOR SIGNAL PEAK DETECTION USING ARTIFICIAL NEURAL NETWORK
    Adam, A.
    Ibrahim, Z.
    Mokhtar, N.
    Shapiai, M. I.
    Mubin, M.
    NEURAL NETWORK WORLD, 2016, 26 (01) : 67 - 89
  • [33] Vehicle Detection and Tracking using Corner Feature Points and Artificial Neural Networks for a Vision-based Contactless Apprehension System
    Billones, Robert Kerwin C.
    Bandala, Argel A.
    Sybingco, Edwin
    Gan Lim, Laurence A.
    Fillone, Alexis D.
    Dadios, Elmer P.
    2017 COMPUTING CONFERENCE, 2017, : 688 - 691
  • [34] Artificial neural network-based approach for detection and classification of defects in polymeric composites using machine vision in SEM study
    Bose, Saswata
    Das, Chapal Kumar
    Shome, Deepayan
    INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY, 2010, 38 (04): : 337 - 361
  • [35] Research on Concrete Beam Damage Detection Using Convolutional Neural Networks and Vibrations from ABAQUS Models and Computer Vision
    Bai, Xin
    Zhang, Zi
    BUILDINGS, 2025, 15 (02)
  • [36] Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System
    Viejo, Claudia Gonzalez
    Torrico, Damir D.
    Dunshea, Frank R.
    Fuentes, Sigfredo
    BEVERAGES, 2019, 5 (02):
  • [37] Misalignment Detection of Rotating Machine Shaft Using Artificial Neural Network and t-Distributed Stochastic Neighbor Embedding Classification Technique
    Lee, Yong Eun
    Zhang, Shujun
    Choi, Nak Joon
    Noh, Yoojeong
    Kim, Kyung Chun
    JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, 2023, 34 (02) : 376 - 383
  • [38] Misalignment Detection of Rotating Machine Shaft Using Artificial Neural Network and t-Distributed Stochastic Neighbor Embedding Classification Technique
    Yong Eun Lee
    Shujun Zhang
    Nak Joon Choi
    Yoojeong Noh
    Kyung Chun Kim
    Journal of Control, Automation and Electrical Systems, 2023, 34 : 376 - 383
  • [39] Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models
    Oğuz Demiryürek
    Erdem Koç
    Fibers and Polymers, 2009, 10 : 237 - 245
  • [40] Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models
    Demiryurek, Oguz
    Koc, Erdem
    FIBERS AND POLYMERS, 2009, 10 (02) : 237 - 245