NDLI: Bifurcations in morris-lecar model exposed to DC electric field

Content Provider	IEEE Xplore Digital Library
Author	Yan-Qiu Che Jiang Wang Hui-Yan Li Xi-Le Wei Bing Deng Feng Dong
Copyright Year	2009
Description	Author affiliation: Department of Automation Engineering, Tianjin University of Technology and Education, Tianjin, P. R. China (Hui-Yan Li) \|\| School of Electrical Engineering and Automation, Tianjin University, Tianjin, P. R. China (Yan-Qiu Che; Jiang Wang; Xi-Le Wei; Bing Deng; Feng Dong)
Abstract	As an important neuron model, the Morris-Lecar (ML) equations can exhibit classes I and II excitabilities with appropriate system parameters. In this paper, the effects of external DC electric field on the neuro-computational properties of ML model are investigated using bifurcation analysis. We obtain the bifurcation diagram in two dimensional parameter space of externally applied DC current and trans-membrane potential induced by external DC electric field. The bifurcation sets partition the two dimensional parameter space about the qualitatively different behaviors of the ML model. Thus the neuron’s information encodes the stimulus information, and vice versa, which is significant in neural control. Furthermore, we identify the electric field as a key parameter to control the transitions among four different excitability and spiking properties.
Starting Page	3433
Ending Page	3436
File Size	532678
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5332510
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bifurcation Neurons Frequency Automation Biological system modeling Mathematical model USA Councils Equations Computational modeling Large-scale systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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