Nonlinear dynamics of a small biological neural network

被引:0
|
作者
Selverston, AI [1 ]
Rabinovich, MI [1 ]
Abarbanel, HDI [1 ]
机构
[1] Univ Calif San Diego, Inst Nonlinear Sci, La Jolla, CA 92093 USA
来源
EXPERIMENTAL CHAOS | 2002年 / 622卷
关键词
D O I
暂无
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We examine the role of chaos in a small biological central pattern generating network in which all of the neurons and their connections are known. The lobster stomatogaatric ganglion has 30 neurons, 14 of which form the pyloric central pattern generator (CPG). This CPG produces a three phase rhythm at a frequency of about 1 Hz. Although CPGs of this type can produce such rhythms entirely independent of sensory feedback or commands from higher centers, they generally produce oscillatory synchronized activity when modulated by chemical substances acting as hormones or released directly from neurons that have inputs to the ganglion. Sensory input acts to control the output of the CPG on a cycle-by-cycle basis. We demonstrate first of all that individual identified neurons that have been isolated from their synaptic inputs are low dimensional and behave chaotically over a large range of their operating regimes. The neurons can be regularized by connecting them to other neurons in the circuit with chemical or electrical synapses. We have modeled individual and small ensembles of pyloric neurons with both Hodgkin-Huxley and Hindmarsh-Rose type models. The latter has also been implemented in analogue hardware to construct electronic neurons. These electronic neurons are very similar to the biological neurons in their dynamical properties and when interfaced to the biological neurons, form hybrid circuits that can function normally with the electronic neurons taking the place of missing or damaged biological neurons. Supported by NIH, ONR, DOE and CIA.
引用
收藏
页码:122 / 138
页数:17
相关论文
共 50 条
  • [21] Neural network approach for solving nonlinear eigenvalue problems of structural dynamics
    Jeswal, S. K.
    Chakraverty, S.
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (14): : 10669 - 10677
  • [22] Nonlinear robust control for a small unmanned helicopter based on neural network
    Xian B.
    Zhang H.-N.
    Kongzhi yu Juece/Control and Decision, 2018, 33 (04): : 627 - 632
  • [23] Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network
    Lauri Salmela
    Nikolaos Tsipinakis
    Alessandro Foi
    Cyril Billet
    John M. Dudley
    Goëry Genty
    Nature Machine Intelligence, 2021, 3 : 344 - 354
  • [24] Neural network approach for solving nonlinear eigenvalue problems of structural dynamics
    S. K. Jeswal
    S. Chakraverty
    Neural Computing and Applications, 2020, 32 : 10669 - 10677
  • [25] Physics-Informed Neural Network for Nonlinear Dynamics in Fiber Optics
    Jiang, Xiaotian
    Wang, Danshi
    Fan, Qirui
    Zhang, Min
    Lu, Chao
    Lau, Alan Pak Tao
    LASER & PHOTONICS REVIEWS, 2022, 16 (09)
  • [26] The effects of nonlinear interactions and network structure in small group opinion dynamics
    Gabbay, Michael
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2007, 378 (01) : 118 - 126
  • [27] Neural network dynamics
    Vogels, TP
    Rajan, K
    Abbott, LF
    ANNUAL REVIEW OF NEUROSCIENCE, 2005, 28 : 357 - 376
  • [28] Nonlinear dynamics and coherent resonance in a network of coupled neural-like oscillators
    Andreev, Andrey, V
    Runnova, Anastasia E.
    Pisarchik, Alexander N.
    Hramov, Alexander E.
    DYNAMICS AND FLUCTUATIONS IN BIOMEDICAL PHOTONICS XV, 2018, 10493
  • [29] Feed-forward neural network as nonlinear dynamics integrator for supercontinuum generation
    Salmela, Lauri
    Hary, Mathilde
    Mabed, Mehdi
    Foi, Alessandro
    Dudley, John M.
    Genty, Goery
    OPTICS LETTERS, 2022, 47 (04) : 802 - 805
  • [30] Dealing with fault dynamics in Nonlinear systems via double neural network units
    Song, YD
    Liao, XH
    Bolden, C
    Yang, Z
    ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS, 2005, 3498 : 92 - 97