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論文

Computational inferences on alteration of neurotransmission in chemotaxis learning in ${it Caenorhabditis elegans}$

鈴木 芳代; 坂下 哲哉; 辻 敏夫*; 小林 泰彦

Lecture Notes in Computer Science 6352, p.291 - 300, 2010/09

${it Caenorhabditis elegans}$ changes its NaCl-associated behavior from attraction to avoidance following exposure to NaCl in the absence of food (salt chemotaxis learning). To understand the changes induced by chemotaxis learning at the neuronal network level, we modeled a neuronal network of chemotaxis and estimated the changes that occurred in the nervous system by comparing the neuronal connection weights prior to and after chemotaxis learning. Our results revealed that neurotransmission involving ASE and AIA neurons differed prior to and after chemotaxis learning. This partially corresponded to the experimental findings of previous studies. In addition, our computational inference results suggest the involvement of novel synapse connections in chemotaxis learning. Our approach to estimate changes of neurotransmission corresponding to learning may help in planning experiments in order of importance.

論文

A Novel tuning method for neural oscillators with a ladder-like structure based on oscillation analysis

服部 佑哉; 鈴木 芳代; 曽 智*; 小林 泰彦; 辻 敏夫*

Lecture Notes in Computer Science 6352, p.401 - 410, 2010/09

Neural oscillators with a ladder-like structure is one of the central pattern generator (CPG) model that is used to simulate rhythmic movements in living organisms. However, it is not easy to realize rhythmical cycles by tuning many parameters of neural oscillators. In this study, we propose an automatic tuning method. We derive the tuning rules for both the time constants and the coefficients of amplitude by linearizing the nonlinear equations of the neural oscillators. Other parameters such as neural connection weights are tuned using a genetic algorithm (GA). Through numerical experiments, we confirmed that the proposed tuning method can successfully tune all parameters.

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