Reproducible sequence generation in random neural ensembles

被引:60
|
作者
Huerta, R [1 ]
Rabinovich, M
机构
[1] Univ Calif San Diego, Inst Nonlinear Sci, La Jolla, CA 92093 USA
[2] Univ Autonoma Madrid, ETS Ingn Informat, GNB, E-28049 Madrid, Spain
关键词
D O I
10.1103/PhysRevLett.93.238104
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Little is known about the conditions that neural circuits have to satisfy to generate reproducible sequences. Evidently, the genetic code cannot control all the details of the complex circuits in the brain. In this Letter, we give the conditions on the connectivity degree that lead to reproducible and robust sequences in a neural population of randomly coupled excitatory and inhibitory neurons. In contrast to the traditional theoretical view we show that the sequences do not need to be learned. In the framework proposed here just the averaged characteristics of the random circuits have to be under genetic control. We found that rhythmic sequences can be generated if random networks are in the vicinity of an excitatory-inhibitory synaptic balance. Reproducible transient sequences, on the other hand, are found far from a synaptic balance.
引用
收藏
页码:238104 / 1
页数:4
相关论文
共 50 条
  • [41] Random optimized geometric ensembles
    Li, Yujian
    Meng, Dongxia
    Gui, Zhiming
    NEUROCOMPUTING, 2012, 94 : 159 - 163
  • [42] MODELING AND CREDIBILITY OF RANDOM ENSEMBLES
    KHEIR, NA
    HOLMES, WM
    SIMULATION, 1982, 38 (03) : 93 - 103
  • [43] Software generation of random numbers by using neural network
    Chan, CK
    Chan, CK
    Cheng, LM
    ARTIFICIAL NEURAL NETS AND GENETIC ALGORITHMS, 2001, : 209 - 211
  • [44] Correlation analysis of random number sequences based on pseudo random binary sequence generation
    Horan, DM
    Guinee, RA
    Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, 2005, : 82 - 86
  • [45] STATISTICAL ENSEMBLES FOR SEQUENCE VARIABILITY
    BERG, OG
    JOURNAL OF MOLECULAR BIOLOGY, 1987, 193 (04) : 743 - 750
  • [46] Random-bit sequence generation from image data
    Alsultanny, Yas Abbas
    IMAGE AND VISION COMPUTING, 2008, 26 (04) : 592 - 601
  • [47] Pseudo Random Sequence Generation From a New Chaotic System
    Cai, Bozhen
    Wang, Guangyi
    Yuan, Fang
    2015 IEEE 16TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT), 2015, : 863 - 867
  • [48] Cellular automata based quasi-random sequence generation
    Zhang, CW
    Lin, LB
    ICCC2004: Proceedings of the 16th International Conference on Computer Communication Vol 1and 2, 2004, : 547 - 550
  • [49] Local Sampling with Momentum Accounts for Human Random Sequence Generation
    Castillo, Lucas
    León-Villagrá, Pablo
    Chater, Nick
    Sanborn, Adam N.
    Proceedings of the 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021, 2021, : 1956 - 1962
  • [50] Disassembly sequence generation using a neural network approach
    Huang, HHT
    Wang, MH
    Johnson, MR
    JOURNAL OF MANUFACTURING SYSTEMS, 2000, 19 (02) : 73 - 82