EI、Scopus 收录
中文核心期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

神经能量与神经信息之间内在动力学关系初探

郑锦超 王如彬 张志康

郑锦超, 王如彬, 张志康. 神经能量与神经信息之间内在动力学关系初探[J]. 力学学报, 2012, 44(5): 919-927. doi: 10.6052/0459-1879-12-046
引用本文: 郑锦超, 王如彬, 张志康. 神经能量与神经信息之间内在动力学关系初探[J]. 力学学报, 2012, 44(5): 919-927. doi: 10.6052/0459-1879-12-046
Zheng Jinchao, Wang Rubin, Zhang Zhikang. THE FIRST EXPLORATION OF THE DYNAMIC RELATION BETWEEN NERVOUS ENERGY AND NEURAL INFORMATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(5): 919-927. doi: 10.6052/0459-1879-12-046
Citation: Zheng Jinchao, Wang Rubin, Zhang Zhikang. THE FIRST EXPLORATION OF THE DYNAMIC RELATION BETWEEN NERVOUS ENERGY AND NEURAL INFORMATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(5): 919-927. doi: 10.6052/0459-1879-12-046

神经能量与神经信息之间内在动力学关系初探

doi: 10.6052/0459-1879-12-046
基金项目: 国家自然科学基金资助项目(10872068,11232005).
详细信息
    通讯作者:

    王如彬

  • 中图分类号: Q189

THE FIRST EXPLORATION OF THE DYNAMIC RELATION BETWEEN NERVOUS ENERGY AND NEURAL INFORMATION

Funds: The project was supported by the National Natural Science Foundation of China (10872068,11232005).
  • 摘要: 根据信息论的基本原理和方法,运用最小互信息和最大熵原理对神经编码进行研究和分析.通过对两个原理的基本介绍,描述了最小互信息和最大熵原理是如何用于评估神经反应中的信息量.研究结果表明神经信息的表达和神经能量的利用率密切相关,并发现高度进化的神经系统在能量的消耗和利用上严格遵循着经济性和高效性两个基本原则.为了验证神经信息处理与能量利用率的关系,提出了信能比的新概念,用于衡量最大熵原理对应神经系统在能量利用率上的经济性和高效性.并通过数值计算证实了一个猜想,即神经系统所消耗的能量反映了神经信息处理的内在规律,这为进一步研究一种崭新的神经信息处理原理——能量神经编码奠定了重要的理论基础.

     

  • Adrian ED.The impulses produced by sensory nerve ending.The Journal of Physiology,1926,61:49-72
    Gutierrez GA,Gutierrez OR.Pattern completion through phase coding in population neurodynamics.Neural Networks,2003,16:649-656  
    Gruner CM,Johnson DH.Correlation and neural information coding fidelity and efficiency.Neurocomputing,1999,26-27:163-168
    Zeng XH,Luo SW,Li QY.An associative sparse ciding neural network and applications.Neurocomputing,2010,73:684-689  
    Averbeck BB,Lee D.Coding and transmission of information by neural ensembles.TRENS in Neurosciences,2004,27(4):225-230  
    Yakoyama R,Wakui T,Satake R.Prediction of energy demands using neural network with model identification by global optimization.Energy Conversion and Management,2009,50(2):319-327  
    Laughlin SB,Sejnowski TJ.Communication in neuronal networks.Network in Biology,2003,301:1870-1874
    Wang RB,Zhang ZK.Energy coding in biological neural network.Cognitive Neurodynamics,2007,1(3):203-212  
    Toyabe S,Sagawa T,Ueda M,et al.Experimental demonstration of information-to-energy conversion and validation of the generalized Jarzynski equality.Nature Physics,2010,6:988-992  
    Cessac B,Moisy HP,Vieville T.Overview of facts and issues about neural coding by spikes.Journal of Physiology,2010,104:5-18
    Fujita K,Kashimori Y.Population coding of electrosensory stimulus in receptor network.Neurocomputing,2006,69:1206-1210  
    Alexander GD,Miller JP,Aldworth Z,et al.Spike pattern-based coding schemes in the cricket cercal sensory system.Neurocomputing,2002,44-46:373-379
    Richmond B,Wiener M.Recruitment order:a powerful neural ensemble code.Nature Neuroscience,2004,7(2):97-98  
    Cloberson A,Stark E,Vaadia E,et a1.The minimum information principle and its application to neural code analysis.PNAS,2009,106:3490-3495  
    Parra LC,Beck JM,Bell AJ.On the maximization of information flow between spiking neurons.Neural Computation,2009,21:2991-3009  
    Wang RB,Zhang ZK,Chen GR.Energy function and energy evolution on neural population.IEEE Transactions on Neural Networks,2008,19(3):535-538  
    Wang RB,Zhang ZK,Chen GR.Energy coding and energy functions for local activities of the brain.Neurocomputing,2009,73:139-150  
    Bezzi M.Information maximization for exploring neural coding in hippocampus and lateral septum.BioSystems,2005,79:183-189  
    Lenzen M,Murray SA,Korte B,et al.Environmental impact assessment including indirect effects-a case study using input-output analysis.Environmental Impact Assessment Review,2003,23:263-282  
    Yang ZY,Chan CW.Simultaneous estimation of the input and output frequencies of nonlinear systems.Automatica,2008,44:1822-1830  
    Liang TC.Monotone empirical Bayes tests for a discrete normal distribution.Statistics & Probability Letters,1999,44:241-249  
    Ferry B,Lahiri P.On measures of uncertainty of empirical Bayes small-area estimators.Journal of Statistics Planning and Inference,2003,112:63-76  
    Chacron MJ,Longtin A,Maler L.Efficient computation via sparse coding in electrosensory neural networks.Current Opinion in Neurobiology,2011,21:1-9  
    Nie YF,Ge JH,Wang Y.Iterative SNR estimation using a priori information.Digital Signal Processing,2009,19:278-286  
    Wang K,Zhang XD.Blind noise variance and SNR estimation for OFDM systems based on information theoretic criteria.Signal Processing,2010,90:2766-2772  
    Covey E.Neural population coding and auditory temporal pattern analysis.Physiology & Behavior,2000,69:211-220  
    Panzeri S,Magri C,Logothetis NK.On the use of information theory for the analysis of the relationship between neural and imaging signals.Magnetic Resonance Imaging,2008,26:1015-1025
  • 加载中
计量
  • 文章访问数:  1753
  • HTML全文浏览量:  43
  • PDF下载量:  752
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-02-21
  • 修回日期:  2012-04-12
  • 刊出日期:  2012-09-18

目录

    /

    返回文章
    返回