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Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions.

Authors :
Zhu, Jun-Yao
Li, Meng-Meng
Zhang, Zhi-Heng
Liu, Gang
Wan, Hong
Source :
Entropy. Jul2023, Vol. 25 Issue 7, p994. 20p.
Publication Year :
2023

Abstract

Objective: Phase transfer entropy ( T E θ ) methods perform well in animal sensory–spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the T E θ methods. Specifically, four T E θ 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 T E θ 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. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The T E θ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4–12 Hz) in visual–spatial associative learning. Significance: These outcomes provide a reference for the T E θ methods in detecting the interactions between brain areas. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10994300
Volume :
25
Issue :
7
Database :
Academic Search Index
Journal :
Entropy
Publication Type :
Academic Journal
Accession number :
168601221
Full Text :
https://doi.org/10.3390/e25070994