[Analysis of oscillatory pattern based on neural network and its applications]

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cognitive Science

Background:

• Neural oscillations are dynamic phenomena fundamental to nervous system function.
• They play critical roles in higher cognitive activities, including information processing, memory consolidation, and integration.
• Altered neural oscillation patterns are linked to cognitive function changes.

Purpose of the Study:

• To review and classify quantitative analytic algorithms for neural oscillation patterns.
• To provide insights into methods used in neurobiology and cognitive science research.
• To discuss potential applications of multidimensional analytic algorithms.

Main Methods:

• Review of commonly employed analytic algorithms for neural oscillations.
• Classification of algorithms based on metrics, synchronization, and coupling modes.
• Discussion of multidimensional algorithms for future applications.

Main Results:

• Identified and categorized various quantitative methods for analyzing neural oscillations.
• Highlighted the importance of these analyses in understanding brain function and dysfunction.
• Presented a framework for classifying algorithms based on their analytical approaches.

Conclusions:

• Quantitative analysis of neural oscillations is essential for computational neuroscience.
• The reviewed algorithms offer diverse approaches to studying brain dynamics.
• Further development and application of multidimensional algorithms hold promise for advancing neurobiological and cognitive research.