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

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

华东理工大学理学院认知神经动力学研究所, 上海 200237

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
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- 文章访问数: 1753
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- 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

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

Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai 200237, China

Funds: The project was supported by the National Natural Science Foundation of China (10872068,11232005).

摘要

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.
- 神经编码 /
- 信息论 /
- 最小互信息 /
- 最大熵 /
- 能量利用率
Abstract: In accordance with the basic principles and methods of Information Theory,this article proposes to research and analyze neural coding by using the minimum mutual information principle and the maximum entropy principle.In this paper,firstly,we elaborate their basic principles.Next,we highlight how the minimum mutual information and maximum entropy principle are used to measure the information in neural responses.Studies show that neural information expression and energy utilization rate are closely related,and the fact that highly evolved nervous system on energy consumption is strictly compliance with the two basic principles-economy and efficiency.In order to verify the relation between the neural information processing and energy utilization,this paper put forward a new concept used to measure the efficiency on energy utilization when the neural system follows the maximum entropy principles.Finally,we demonstrate a conjecture that the consumed energy in nervous system reflects the inherent law of neural information energy through the numeric calculation.It lays an important foundation for the further study of a new neural information processing principle—energy neural coding.
- neural coding /
- information theory /
- minimum mutual information /
- maximum entropy /
- energy utilization rate

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参考文献(27)

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

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