Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics
Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics
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DOI：
10.3389/fncom.2017.00053
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发表时间：
2017-06-13
影响因子：
3.2
通讯作者：
Kutz, J. Nathan
中科院分区：
文献类型：
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作者：
Kunert-Graf, James M.;Shlizerman, Eli;Kutz, J. Nathan
线虫线虫的神经动力学在实验上是低维的，可以理解为多个低维吸引子之间的长时间尺度转换。以前的建模工作已经发现，蠕虫完整神经网络的动态模型能够对某些输入产生合理的动态反应，即使所有神经元被视为相同的，除了它们的连接性。这项研究研究了线虫神经动力学的这样一个模型，发现对不同的输入产生了各种各样的多稳态反应。具体地说，我们生成了所有可能的单神经元输入的分岔图，显示了不同输入区域的不动点和极限环的存在。根据接受输入的神经元类型的不同，动态反应的性质被认为是不同的；例如，输入到感觉神经元的输入比输入到运动神经元更有可能驱动系统中的分叉。作为一个具体的例子，我们考虑了对神经元对PLM和ASK的复合输入，发现了极限环和不动点的双稳定性。接近这些状态的瞬时时标比系统的任何固有时标都长得多。这表明我们的模型与神经系统中动力学的描述是一致的，即离散的、低维的吸引子之间对应于行为状态的长时间尺度转换。
The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous modeling work has found that dynamic models of the worm's full neuronal network are capable of generating reasonable dynamic responses to certain inputs, even when all neurons are treated as identical save for their connectivity. This study investigates such a model of C. elegans neuronal dynamics, finding that a wide variety of multistable responses are generated in response to varied inputs. Specifically, we generate bifurcation diagrams for all possible single-neuron inputs, showing the existence of fixed points and limit cycles for different input regimes. The nature of the dynamical response is seen to vary according to the type of neuron receiving input; for example, input into sensory neurons is more likely to drive a bifurcation in the system than input into motor neurons. As a specific example we consider compound input into the neuron pairs PLM and ASK, discovering bistability of a limit cycle and a fixed point. The transient timescales in approaching each of these states are much longer than any intrinsic timescales of the system. This suggests consistency of our model with the characterization of dynamics in neural systems as long-timescale transitions between discrete, low-dimensional attractors corresponding to behavioral states.