Effects of chemical synapses on the enhancement of signal propagation in coupled neurons near the canard regime

被引:43
作者
Li, Xiumin [1 ]
Wang, Jiang [1 ]
Hu, Wuhua [1 ]
机构
[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China
来源
PHYSICAL REVIEW E | 2007年 / 76卷 / 04期
关键词
D O I
10.1103/PhysRevE.76.041902
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
The response of three coupled FitzHugh-Nagumo neurons, under Gaussian white noise, to a subthreshold periodic signal is studied in this paper. By combining the canard dynamics, chemical coupling, and stochastic resonance together, the information transfer in this neural system is investigated. We find that chemical synaptic coupling is more efficient than the well-known linear coupling (gap junction) for local signal input, i.e., only one of the three neurons is subject to the periodic signal. This weak and local input is common in biological systems for the sake of low energy consumption.
引用
收藏
页数:6
相关论文
共 25 条
[1]  
[Anonymous], THESIS POTSDAM U
[2]   Role of chemical synapses in coupled neurons with noise -: art. no. 021901 [J].
Balenzuela, P ;
García-Ojalvo, J .
PHYSICAL REVIEW E, 2005, 72 (02)
[3]   Phase switching in a system of two noisy Hodgkin-Huxley neurons coupled by a diffusive interaction -: art. no. 061917 [J].
Casado, JM ;
Baltanás, JP .
PHYSICAL REVIEW E, 2003, 68 (06)
[4]  
Cronin J., 1987, MATH ASPECTS HODGKIN, DOI DOI 10.1017/CBO9780511983955
[5]   Analysis of a canard mechanism by which excitatory synaptic coupling can synchronize neurons at low firing frequencies [J].
Drover, J ;
Rubin, J ;
Su, JH ;
Ermentrout, B .
SIAM JOURNAL ON APPLIED MATHEMATICS, 2004, 65 (01) :69-92
[6]   IMPULSES AND PHYSIOLOGICAL STATES IN THEORETICAL MODELS OF NERVE MEMBRANE [J].
FITZHUGH, R .
BIOPHYSICAL JOURNAL, 1961, 1 (06) :445-&
[7]   Stochastic resonance [J].
Gammaitoni, L ;
Hanggi, P ;
Jung, P ;
Marchesoni, F .
REVIEWS OF MODERN PHYSICS, 1998, 70 (01) :223-287
[8]   Global dynamics and stochastic resonance of the forced FitzHugh-Nagumo neuron model [J].
Gong, PL ;
Xu, JX .
PHYSICAL REVIEW E, 2001, 63 (03)
[9]   An algorithmic introduction to numerical simulation of stochastic differential equations [J].
Higham, DJ .
SIAM REVIEW, 2001, 43 (03) :525-546
[10]  
Izhikevich E. M., 2005, Dynamical systems in neuroscience