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Title: Study on chemotaxis behaviors of C. elegans using dynamic neural network models: From artificial to biological model
Authors: Xu, J.-X. 
Deng, X.
Keywords: C. elegans
Dynamic Neural Network
Issue Date: Oct-2010
Citation: Xu, J.-X., Deng, X. (2010-10). Study on chemotaxis behaviors of C. elegans using dynamic neural network models: From artificial to biological model. Journal of Biological Systems 18 (SPEC. ISSUE 1) : 3-33. ScholarBank@NUS Repository.
Abstract: With the anatomical understanding of the neural connection of the nematode Caenorhabditis elegans (C. elegans), its chemotaxis behaviors are investigated in this paper through the association with the biological nerve connections. The chemotaxis behaviors include food attraction, toxin avoidance and mixed-behaviors (finding food and avoiding toxin concurrently). Eight dynamic neural network (DNN) models, two artifical models and six biological models, are used to learn and implement the chemotaxis behaviors of C. elegans. The eight DNN models are classified into two classes with either single sensory neuron or dual sensory neurons. The DNN models are trained to learn certain switching logics according to different chemotaxis behaviors using real time recurrent learning algorithm (RTRL). First we show the good performance of the two artifical models in food attraction, toxin avoidance and the mixed-behaviors. Next, six neural wire diagrams from sensory neurons to motor neurons are extracted from the anatomical nerve connection of C. elegans. Then the extracted biological wire diagrams are trained using RTRL directly, which is the first time in this field of research by associating chemotaxis behaviors with biological neural models. An interesting discovery is the need for a memory neuron when single-sensory models are used, which is consistent with the anatomical understanding on a specific neuron that functions as a memory. In the simulations, the chemotaxis behaviors of C. elegans can be depicted by several switch logical functions which can be learned by RTRL for both artifical and biological models. © 2010 World Scientific Publishing Company.
Source Title: Journal of Biological Systems
ISSN: 02183390
DOI: 10.1142/S0218339010003597
Appears in Collections:Staff Publications

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