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首页> 外文期刊>Journal of Computational Neuroscience >Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model
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Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model

机译：网络拓扑对大规模皮层网络模型模拟的多神经突峰数据推断突触连通性的影响

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Many mechanisms of neural processing rely critically upon the synaptic connectivity between neurons. As our ability to simultaneously record from large populations of neurons expands, the ability to infer network connectivity from this data has become a major goal of computational neuroscience. To address this issue, we employed several different methods to infer synaptic connections from simulated spike data from a realistic local cortical network model. This approach allowed us to directly compare the accuracy of different methods in predicting synaptic connectivity. We compared the performance of model-free (coherence measure and transfer entropy) and model-based (coupled escape rate model) methods of connectivity inference, applying those methods to the simulated spike data from the model networks with different network topologies. Our results indicate that the accuracy of the inferred connectivity was higher for highly clustered, near regular, or small-world networks, while accuracy was lower for random networks, irrespective of which analysis method was employed. Among the employed methods, the model-based method performed best. This model performed with higher accuracy, was less sensitive to threshold changes, and required less data to make an accurate assessment of connectivity. Given that cortical connectivity tends to be highly clustered, our results outline a powerful analytical tool for inferring local synaptic connectivity from observations of spontaneous activity.
机译：神经处理的许多机制严重依赖于神经元之间的突触连接。随着我们同时从大量神经元进行记录的能力不断扩展，从这些数据推断网络连通性的能力已成为计算神经科学的主要目标。为了解决这个问题，我们采用了几种不同的方法，从真实的本地皮质网络模型的模拟尖峰数据中推断出突触连接。这种方法使我们能够直接比较不同方法在预测突触连接性方面的准确性。我们比较了无模型（相干度量和传递熵）和基于模型（耦合逃逸率模型）的连接推理方法的性能，并将这些方法应用于来自具有不同网络拓扑的模型网络的模拟尖峰数据。我们的结果表明，无论采用哪种分析方法，对于高度聚类，接近规则或小型世界的网络，推断的连接的准确性较高，而对于随机网络的准确性较低。在所采用的方法中，基于模型的方法效果最好。该模型执行的准确性更高，对阈值更改的敏感度较低，并且需要较少的数据才能准确评估连接性。鉴于皮质连接性倾向于高度聚集，我们的研究结果概述了一种强大的分析工具，可根据自发活动的观察结果推断局部突触连接性。

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