Neural Interactome: Interactive Simulation of a Neuronal System
Neural Interactome: Interactive Simulation of a Neuronal System
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DOI：
10.3389/fncom.2019.00008
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发表时间：
2019-03-13
影响因子：
3.2
通讯作者：
Shlizerman, Eli
中科院分区：
文献类型：
--
作者：
Kim, Jimin;Leahy, William;Shlizerman, Eli
连通性和生物物理过程决定了神经元网络的功能。因此，我们开发了一个称为Neural Interactome组（1,2）的实时框架，以同时可视化并与此类网络的结构和动力学相互作用。 Neural Interactome是一个跨平台框架，将图形可视化与神经动力学的模拟或实验记录的多神经时间序列相结合，允许刺激在神经元中应用以检查网络响应。此外，神经相互作用组支持结构变化，例如神经元与网络的断开（消融特征）。可以在单个神经元级别（使用缩放功能），返回时间（使用审查功能）并记录（使用预设功能）上探索神经动力学。神经相互作用组的发展是由通用概念指导的，该概念适用于具有不同神经连通性和动态的神经元网络。我们使用秀丽隐杆线虫（C. elegrans）线虫的神经系统模型（一种模型有机体，具有分辨的连接组和神经动力学）实施了框架。我们表明，神经相互作用组有助于研究与运动和其他刺激相关的神经反应模式。特别是，我们证明了刺激和消融如何有助于识别塑造特定动力学的神经元。我们检查了经过实验研究的场景，例如触摸响应电路，并探索未经详细的实验研究的新场景。
Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome(1,2), to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature). Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature), and recorded (using presets feature). The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism with resolved connectome and neural dynamics. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies.