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|Title:||Study on C. elegans behaviors using recurent neural network model||Authors:||Xu, J.-X.
Recurrent neural network
|Issue Date:||2010||Citation:||Xu, J.-X.,Deng, X.,Ji, D. (2010). Study on C. elegans behaviors using recurent neural network model. 2010 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2010 : 1-6. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCIS.2010.5518591||Abstract:||With the complete knowledge on the anatomical nerve connections of the nematode Caenorhabditis elegans (C. elegans), the chemotaxis behaviors including food attraction and toxin avoidance, are modeled using dyic neural networks (DNN). This paper first uses artificial DNN, with 7 neurons, to model chemotaxis behaviors with single sensor neurons. Real time recurrent learning (RTRL) is carried out to train the DNN weights. Next, this paper split the single sensor neuron into the left and right pair (dual-sensor neuron), with the assumption that C. elegans can distinguish the input diference between left and right, and then the model is applied to learn to reproduce the chemotaxis behaviors. The simulation results conclude that DNN c well model the behaviors of C. elegans from sensor inputs to motor outputs both in single sensor and dual-sensor neuron networks. © 2010 IEEE.||Source Title:||2010 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2010||URI:||http://scholarbank.nus.edu.sg/handle/10635/71888||ISBN:||9781424464999||DOI:||10.1109/ICCIS.2010.5518591|
|Appears in Collections:||Staff Publications|
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