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Multivariate Autoregressive-based Neuronal Network Flow Analysis for In-vitro Recorded Bursts

机译:体外记录爆发的基于多元自回归的神经网络流分析

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Neuroscientific studies of in vitro neuron cell cultures has attracted paramount attention to investigate the behaviour of neuronal networks in response to different environmental conditions and external stimuli such as drugs, optical and electrical stimulations. Microelec-trode array (MEA) technology has been widely adopted as a tool for this investigation. In this work, we present a new approach to estimate inter-connectivity of neural spikes using multivariate autoregressive (MVAR) analysis and Partial Directed Coherence (PDC). The proposed approach has the potential to discover hidden intra-burst causal connectivity patterns and to help understand the spatiotemporal communication patterns within bursts, pre and post stimulations.
机译:体外神经元细胞培养的神经科学研究已经引起了人们的极大关注,以研究神经元网络对不同环境条件和外部刺激(例如药物,光学和电刺激)的反应。微电极阵列(MEA)技术已被广泛用作这项研究的工具。在这项工作中,我们提出了一种使用多元自回归(MVAR)分析和部分定向相干性(PDC)估计神经尖峰相互连接性的新方法。所提出的方法有可能发现隐藏的突发内因果联系模式,并有助于理解突发,刺激前后的时空通信模式。

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