期刊文献+

HH模型阈值特性分析及参数空间拟合 被引量:2

Analysis of threshold characteristics of HH model and parameter fitting.
下载PDF
导出
摘要 通过寻求HH(Hodgkin-Huxley)模型中参数的变化与阈值变化之间的关系,得到神经元的阈值特性.首先,根据神经元阈下离子通道的特性来对四维HH模型做一定的简化,使所得到的二维简化模型保持原有四维HH模型的阈值特性.然后,用相平面法来对简化模型的阈值特性进行定性分析,给出了参数变化与二维简化模型中的鞍点电压值变化之间的关系.最后,把四维HH模型的数值仿真结果与相平面分析结果进行对照,发现可用鞍点附近动力学特性来反映阈值特性.在定性上,这是一种寻求参数变化与阈值变化关系的方法,也是分析阈值下参数空间的手段. The HH (Hodgkin-Huxley) model, based on the neuropsychological characteristics, was used to analyze the threshold behavior of realistic neuron axon and the relationship of changes of the parameter and the threshold. Firstly, the two-dimensional simplified model which hold the characteristics of four-dimensional HH model was obtained. Secondly, the phase planes were introduced to find qualitatively the threshold of dynamical behavior, and the influences of parameters change on saddle point were discussed. Finally, by numerical simulation it was showed that the dynamical characteristics near the saddle point reflects the threshold behavior of four-dimensional HH model. So, a new method is attained to seek the sub-threshold parameter space.
出处 《浙江大学学报(理学版)》 CAS CSCD 2004年第6期685-689,共5页 Journal of Zhejiang University(Science Edition)
基金 973项目(2002CCA01800).
关键词 Hodgkin-Huxley模型 二维简化模型 相平面分析 阈值特性 Hodgkin-Huxley model two dimensional simplified model phase plane analysis threshold characteristics
  • 相关文献

参考文献6

  • 1ABBOTT L F, KEPLER T B. Model neurons: from Hodgkin-Huxley to Hopfield[A]. GARRIDO L. Statistical Mechanics of Neural Networks [C]. Berlin:Springer-Verlag, 1990. 4- 18.
  • 2VERHULST F. Nonlinear Differential Equations and Dynamical Systems [M ]. Berlin: Springer-Verlag,1996.
  • 3NAGUMO J S, ARIMOTO S, YOSHIZAWA S. An active pulse transmission line simulating nerve axon[J]. Proc Inst Radio Engineers, 1962, 50: 2061-2070.
  • 4GERSTNER W, KISTLER W M. Spiking Neuron Models-Single Neurons, Populations, Plasticity [M].Cambridge:Cambridge Univ Press, 2002.
  • 5HODGKIN A, HUXLEY A. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. J Physiol, 1952,117:500-544.
  • 6ERMENTROUT G B. Type I membranes, phase resetting curves, and synchrony [J]. Neural Comput,1996, 8(5): 979-1001.

同被引文献16

  • 1Buck L, Axel R. A novel muhigene family may encode odorant receptors:a molecular basis for odor recognition [J]. Cell, 1991,65(1 ) : 175-187.
  • 2The Nobel Assembly at Karolinska Institutet has today decided to award:The Nobel Prize in Physiology or Medicine for 2004 [EB/OL].http://nobelprize-org/medicine/laureates/2004/press. html.
  • 3Horcholle-Bossavit G,Quenet B,Foucart O. Oscillation and coding in a formal neural network considered as a guide for plausible simulations of the insect olfactory system [J]. Biosystems, 2007,89(1-3) : 244-256.
  • 4Perez-Orive J ,Bazhenov M ,Laurent G. Intrinsic and circuit properties favor coincidence detection for decoding oscillatory input[J]. J Neurosci, 2004,24 (26) :6037-6047.
  • 5Brown SL,Joseph J,Stopfer M. Encoding a temporally structured stimulus with a temporally structured neural representation [J]. Nat Neurosci, 2005,8 ( 11 ) : 1568-1576.
  • 6Laurent G. Olfactory network dynamics and the coding of multidimensional signals[J].Nat Rev Neurosci, 2002,3 ( 11 ) : 884- 895.
  • 7Hodgkin AL,Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve [J]. J Physiol, 1952, 117 (4) :500-544.
  • 8Bazhenov M, Stopfer M, Rabinovich M, et al. Model of transient oscillatory synchronization in the locust antennal lobe [J]. Neuron, 2001,30 (2) : 553-567.
  • 9Destexhe A, Mainen ZF, Sejnowski TJ. Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism [J]. J Comput Neurosci, 1994,1 (3) : 195-230.
  • 10Destexhe A,Bal T, McCormick DA,et al. Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices [J]. J Neurophysiol, 1996,76 (3) :2049-2070.

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部