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首页> 外文期刊>Journal of Computational Neuroscience >Biological modeling of complex chemotaxis behaviors for C. elegans under speed regulation-a dynamic neural networks approach
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Biological modeling of complex chemotaxis behaviors for C. elegans under speed regulation-a dynamic neural networks approach

机译：线虫速度调控下复杂趋化行为的生物学建模-动态神经网络方法

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摘要

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 models are 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.
机译：本文探讨了秀丽隐杆线虫的几种复杂趋化行为的建模，包括食物吸引，避免毒素和运动速度调节。我们首先分别模拟食物吸引和避免毒素的趋化行为。然后，提出了一个集成的趋化行为模型，该模型同时执行两种趋化行为。所提出的趋化行为模型的新颖性和独特性具有几个属性。首先，所有趋化行为模型都是基于生物学的，即拟议的趋化行为模型是通过从感觉神经元到运动神经元中提取神经线图而构建的，其中感觉神经元是趋化行为的特质。其次，趋化行为模型能够执行转弯和速度调节。第三，趋化行为的特征在于一组决定方向和速度的开关逻辑功能。所有模型均使用动态神经网络（DNN）实施，并使用实时递归学习（RTRL）算法进行训练。通过整合速度调节机制，秀丽隐杆线虫可以在接近食物来源或远离毒素时自发停止。测试结果和与实验结果的比较证明，所提出的趋化行为模型能够很好地模仿线虫在不同环境下的趋化行为。

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