DocumentCode :
2774835
Title :
Complex chemotaxis behaviors of C. elegans with speed regulation achieved by dynamic neural networks
Author :
Jian-Xin Xu ; Xin Deng
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
8
Abstract :
This paper explores the complex chemotaxis behaviors of C. elegans. These behaviors include finding food and avoiding toxin simultaneously under either dual-sensory mode or single-sensory mode as well as varying locomotion speed. In dual-sensory mode, the concentration difference between left side and right side is used to determine the orientation. In single-sensory mode, a memory neuron is involved to detect the concentration difference between two time steps for navigation. First, two models are explored, namely, dual-sensory model and single-sensory model. Then, an integrated model is proposed to perform all the chemotaxis behaviors synchronously. These three models are constructed biological by extracting the neural wire diagram from sensory neurons to motor neurons and can perform left turning, right turning, and speed regulation. The chemotaxis behaviors are characterized by a set of switching logic functions that decide orientation and speed. The wire diagrams are depicted as dynamic neural networks (DNN) and trained by the real time recurrent learning (RTRL) algorithm. By incorporating a speed regulation mechanism, C. elegans can stop spontaneously when approaching food or leaving toxin. Test results verify that the biological models can well mimic the chemotaxis behaviors of C. elegans.
Keywords :
biology computing; learning (artificial intelligence); recurrent neural nets; C. elegans; DNN; RTRL; biological models; complex chemotaxis behaviors; concentration difference detection; dual-sensory mode; dynamic neural networks; food attraction; memory neuron; neural wire diagram extraction; real time recurrent learning algorithm; single-sensory mode; speed regulation mechanism; toxin avoidance; Biological system modeling; Grippers; Mathematical model; Neurons; Switches; Wires;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252661
Filename :
6252661
Link To Document :
بازگشت