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Complex Chemotaxis Behaviors of C. elegans with Speed Regulation Achieved by Dynamic Neural Networks

机译:C.杆杆线虫的复杂趋化性行为与动态神经网络实现的速度调节

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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.
机译:本文探讨了C.秀丽隐杆线虫的复杂趋化性行为。这些行为包括在双感觉模式或单个感官模式下同时找到食物和避免毒素,以及不同的运动速度。在双感觉模式下,左侧和右侧之间的浓度差异用于确定取向。在单感觉模式下,涉及存储器神经元以检测两个导航步骤之间的浓度差异。首先,探索了两种型号,即双感觉模型和单感觉模型。然后,提出了一种集成模型来同步执行所有趋化性行为。这三种模型通过从感觉神经元到电机神经元提取神经线图来构建生物学,并且可以执行左转,右转弯和速度调节。趋化性行为的特征在于一组切换逻辑功能,可以决定取向和速度。线图被描绘为动态神经网络(DNN),并通过实时复发学习(RTRL)算法训练。通过掺入速度调节机制,C.杆状杆子在接近食物或离开毒素时可以自发地停止。测试结果验证了生物学模型可以深入模仿C.秀丽隐杆线虫的趋化性行为。

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