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Title: Biological modeling of complex chemotaxis behaviors for C. elegans under speed regulation - A dynamic neural networks approach
Authors: Xu, J.-X. 
Deng, X.
Keywords: Biological models
C. elegans
Chemotaxis behaviors
Dynamic neural networks
Neural wire diagram
Issue Date: Aug-2013
Citation: Xu, J.-X., Deng, X. (2013-08). Biological modeling of complex chemotaxis behaviors for C. elegans under speed regulation - A dynamic neural networks approach. Journal of Computational Neuroscience 35 (1) : 19-37. ScholarBank@NUS Repository.
Abstract: In this paper, the modeling of several complex chemotaxis behaviors of C. elegans is explored, which include food attraction, toxin avoidance, and locomotion speed regulation. We first model the chemotaxis behaviors of food attraction and toxin avoidance separately. Then, an integrated chemotaxis behavioral model is proposed, which performs the two chemotaxis behaviors simultaneously. The novelty and the uniqueness of the proposed chemotaxis behavioral models are characterized by several attributes. First, all the chemotaxis behavioral model sare on biological basis, namely, the proposed chemotaxis behavior models are constructed by extracting the neural wire diagram from sensory neurons to motor neurons, where sensory neurons are specific for chemotaxis behaviors. Second, the chemotaxis behavioral models are able to perform turning and speed regulation. Third, chemotaxis behaviors are characterized by a set of switching logic functions that decide the orientation and speed. All models are implemented using dynamic neural networks (DNN) and trained using the real time recurrent learning (RTRL) algorithm. By incorporating a speed regulation mechanism, C. elegans can stop spontaneously when approaching food source or leaving away from toxin. The testing results and the comparison with experiment results verify that the proposed chemotaxis behavioral models can well mimic the chemotaxis behaviors of C. elegans in different environments. © 2013 Springer Science+Business Media New York.
Source Title: Journal of Computational Neuroscience
ISSN: 09295313
DOI: 10.1007/s10827-012-0437-1
Appears in Collections:Staff Publications

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