论文标题
在生物学启发的神经网络中量化同步
Quantifying Synchronization in a Biologically Inspired Neural Network
论文作者
论文摘要
我们提出了一组整合的算法，以获得大脑时间序列数据中同步的客观度量。算法在MATLAB中实现；我们将一组“工具”集称为Syncbox。我们的Syncbox动机是了解现有种群神经网络中的潜在动态，通常称为神经质量模型，这些动态模仿了视觉丘脑组织的局部田间电位。具体而言，我们旨在在对周期性刺激的模型响应中客观地测量相位同步。这是为了模仿与周期性刺激相对应的头皮脑电图（EEG）的稳态诱发的电位（SSVEP）的状况。我们在现有的视觉丘脑神经质量模型上展示了Syncbox的使用。在我们成功测试Syncbox之后，目前正在使用它用于了解增强视觉途径神经网络中的基本动态的进一步研究
We present a collated set of algorithms to obtain objective measures of synchronisation in brain time-series data. The algorithms are implemented in MATLAB; we refer to our collated set of 'tools' as SyncBox. Our motivation for SyncBox is to understand the underlying dynamics in an existing population neural network, commonly referred to as neural mass models, that mimic Local Field Potentials of the visual thalamic tissue. Specifically, we aim to measure the phase synchronisation objectively in the model response to periodic stimuli; this is to mimic the condition of Steady-state-visually-evoked-potentials (SSVEP), which are scalp Electroencephalograph (EEG) corresponding to periodic stimuli. We showcase the use of SyncBox on our existing neural mass model of the visual thalamus. Following our successful testing of SyncBox, it is currently being used for further research on understanding the underlying dynamics in enhanced neural networks of the visual pathway
