Neuronal coding and spiking randomness

被引:59
|
作者
Kostal, Lubomir
Lansky, Petr
Rospars, Jean-Pierre
机构
[1] Acad Sci Czech Republ, Inst Physiol, Prague 14220 4, Czech Republic
[2] INRA, UMPC, UMR 1272, INRA AgroParisTech Physiol Insecte,Signalizat & C, F-78026 Versailles, France
关键词
entropy; randomness; spike train; variability; INFORMATION-THEORETIC ANALYSIS; STATISTICAL-ANALYSIS; CORTICAL-NEURONS; ENTROPY; DISTRIBUTIONS; TRAINS; VARIABILITY; SEQUENCES; INTERVALS; LEIBLER;
D O I
10.1111/j.1460-9568.2007.05880.x
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Fast information transfer in neuronal systems rests on series of action potentials, the spike trains, conducted along axons. Methods that compare spike trains are crucial for characterizing different neuronal coding schemes. In this paper we review recent results on the notion of spiking randomness, and discuss its properties with respect to the rate and temporal coding schemes. This method is compared with other widely used characteristics of spiking activity, namely the variability of interspike intervals, and it is shown that randomness and variability provide two distinct views. We demonstrate that estimation of spiking randomness from simulated and experimental data is capable of capturing characteristics that would otherwise be difficult to obtain with conventional methods.
引用
收藏
页码:2693 / 2701
页数:9
相关论文
共 50 条
  • [21] Creating Randomness with Games
    Henno, Jaak
    Jaakkola, Hannu
    Makela, Jukka
    2019 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS (INES 2019), 2019, : 331 - 337
  • [22] Correlations in spiking neuronal networks with distance dependent connections
    Kriener, Birgit
    Helias, Moritz
    Aertsen, Ad
    Rotter, Stefan
    JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2009, 27 (02) : 177 - 200
  • [23] The effect of randomness in complex models
    Harlow, DG
    NINTH ISSAT INTERNATIONAL CONFERENCE ON RELIABILITY AND QUALITY IN DESIGN, 2003 PROCEEDINGS, 2003, : 284 - 288
  • [24] The power law statistics of the spiking timing in a neuronal network
    Yao, Chenggui
    Sun, Jianqiang
    Jin, Jun
    Shuai, Jianwei
    Li, Xiang
    Yao, Yuangen
    Xu, Xufan
    CHAOS SOLITONS & FRACTALS, 2023, 172
  • [25] A divergence formula for randomness and dimension
    Lutz, Jack H.
    THEORETICAL COMPUTER SCIENCE, 2011, 412 (1-2) : 166 - 177
  • [26] Automated customization of large-scale spiking network models to neuronal population activity
    Wu, Shenghao
    Huang, Chengcheng
    Snyder, Adam C.
    Smith, Matthew A.
    Doiron, Brent
    Yu, Byron M.
    NATURE COMPUTATIONAL SCIENCE, 2024, 4 (09): : 690 - 705
  • [27] Quantifying randomness in real networks
    Orsini, Chiara
    Dankulov, Marija M.
    Colomer-de-Simon, Pol
    Jamakovic, Almerima
    Mahadevan, Priya
    Vahdat, Amin
    Bassler, Kevin E.
    Toroczkai, Zoltan
    Boguna, Marian
    Caldarelli, Guido
    Fortunato, Santo
    Krioukov, Dmitri
    NATURE COMMUNICATIONS, 2015, 6
  • [28] Neural Signatures: Multiple Coding in Spiking–bursting Cells
    Roberto Latorre
    Francisco B. Rodríguez
    Pablo Varona
    Biological Cybernetics, 2006, 95 : 169 - 183
  • [29] Randomness complexity of private computation
    Blundo, C
    De Santis, A
    Persiano, G
    Vaccaro, U
    COMPUTATIONAL COMPLEXITY, 1999, 8 (02) : 145 - 168
  • [30] Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations
    Hesselmann, Guido
    Sadaghiani, Sepideh
    Friston, Karl J.
    Kleinschmidt, Andreas
    PLOS ONE, 2010, 5 (03):