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Complex chemotaxis behaviors of C. elegans with speed regulation achieved by dynamic neural networks

机译:动态神经网络实现速度调节的线虫的复杂趋化行为

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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:本文探讨线虫的复杂趋化行为。这些行为包括在双感官模式或单感官模式下以及不断变化的运动速度下同时寻找食物和避免毒素。在双感应模式下,左侧和右侧之间的浓度差用于确定方向。在单传感器模式下,涉及记忆神经元以检测两个时间步长之间的浓度差以进行导航。首先,探讨了两种模型,即双感官模型和单感官模型。然后,提出了一个集成模型来同步执行所有趋化行为。通过提取从感觉神经元到运动神经元的神经线图可以生物学地构建这三个模型,并且可以执行左转,右转和速度调节。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。线图描绘为动态神经网络(DNN),并通过实时递归学习(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或离开毒素时自发停止。测试结果证明,该生物学模型可以很好地模仿秀丽隐杆线虫的趋化行为。

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