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A model of chemotaxis and associative learning in C. elegans

机译:秀丽隐杆线虫趋化性和联想学习的模型

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The nematode C. elegans has attracted a great deal of interest from the neuroscience community due to the simplicity of its nervous system, which in the hermaphrodite is composed of just 302 neurons. C. elegans is known to engage in a number of sophisticated behaviours such as chemo- and thermotaxis. Experimental work has shown that these behaviours can be modified by experience and that C. elegans is capable of associative learning. In this paper, we focus on the chemotactic response of C. elegans to sodium chloride mediated by the ASE sensory neurons. We construct a biophysical model of the ASEL and ASER neurons that captures the time course of the ASE responses in response to up- and down-steps in NaCl concentration. We use this model to show that the time course of the ASE responses provide sufficient temporal resolution to successfully drive chemotaxis in C. elegans via steering, pirouettes and control of final turn angle. We show that these different locomotion strategies are individually capable of driving chemotaxis and that by working together they produce the best chemotactic response. We find that there is a separation into upward and downward drives mediated by the left and right ASE neurons. We show that the connectivity from ASEL and ASER must be of opposite polarity and that ASER, and the concomitant ability to sense when the worm is moving down the gradient, is more important for chemotaxis than ASEL, findings that are consistent with existing modelling studies in the literature. Finally, we examine associative learning in the network and show that experimental data can be explained by changes that occur at either the synaptic or sensory neuron level, the choice of which has distinct consequences for network function.
机译:线虫秀丽隐杆线虫由于其神经系统的简单性而引起了神经科学界的极大兴趣,该系统的雌雄同体仅由302个神经元组成。众所周知,秀丽隐杆线虫会参与许多复杂的行为,例如化学趋同性和趋热性。实验工作表明,可以通过经验修改这些行为,并且秀丽隐杆线虫能够进行联想学习。在本文中,我们集中于秀丽隐杆线虫对由ASE感觉神经元介导的氯化钠的趋化反应。我们构建了一个ASEL和ASER神经元的生物物理模型,该模型捕获了NaCl浓度上升和下降时ASE反应的时间过程。我们使用此模型显示ASE响应的时程可提供足够的时间分辨率,以通过操纵,旋转旋转和最终转向角的控制成功地驱动秀丽隐杆线虫的趋化性。我们表明,这些不同的运动策略分别具有驱动趋化性的能力,并且通过共同努力,它们可产生最佳的趋化性反应。我们发现由左和右ASE神经元介导的向上和向下驱动器分离。我们表明,ASEL与ASER的连通性必须具有相反的极性,并且ASER以及伴随感测蠕虫何时沿梯度向下移动的能力比ASEL对趋化性更为重要,这一发现与ASEL中现有的建模研究一致文献。最后,我们检查了网络中的联想学习,并表明可以通过突触或感觉神经元水平发生的变化来解释实验数据,而这些变化的选择对网络功能具有明显的影响。

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