Zhu Jun-Yao, Li Meng-Meng, Zhang Zhi-Heng, Liu Gang, Wan Hong
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China.
Entropy (Basel). 2023 Jun 29;25(7):994. doi: 10.3390/e25070994.
Phase transfer entropy (TEθ) methods perform well in animal sensory-spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual-spatial associative learning. (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4-12 Hz) in visual-spatial associative learning. These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.
相位转移熵（TEθ）方法在动物感觉空间联想学习中表现良好。然而，它们的优缺点仍不明确，这限制了它们的应用。本文提出了TEθ方法的性能基线。具体而言，将四种TEθ方法应用于神经团块模型生成的模拟信号以及来自具有已知相互作用特性的雪貂的实际神经数据，以研究TEθ方法在识别方向耦合时的准确性、稳定性和计算复杂性。然后，根据性能基线选择最合适的方法，并将其用于从鸽子记录的局部场电位，以检测视觉空间联想学习中海马体（Hp）和尾外侧皮质（NCL）之间的相互作用。（1）本文获得了一个性能基线表，其中包含适用于不同场景的最合适方法。（2）TEθ方法在视觉空间联想学习中，在θ频段（4-12赫兹）优先识别出从鸽子的Hp到NCL的信息流。这些结果为TEθ方法检测脑区之间的相互作用提供了参考。
