Modeling macroscopic electroencephalogram dynamics based on the scaling of network dynamics for microscopic neurons

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330293
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Soft computing
• Research Institution: National Institute of Information and Communications Technology
• Principal Investigator: Umehara Hiroaki 国立研究開発法人情報通信研究機構, 脳情報通信融合研究センター脳機能解析研究室, 研究マネージャー (60358942)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 脳神経集団モデル / 脳神経細胞モデル

Outline of Final Research Achievements

To contribute to the decoding of brain function information from electroencephalogram or magnetoencephalogram signals, this study aims at finding a key to solving micro-macro correspondence between the neural activities and the measured signals. Neural mass models are the elementary models for describing macroscopic cortical dynamics. However, the model equation has been used the time constants which are given by an empirical hypothesis from the measurement results and its direct relationship to the microscopic neuron model has not been fully elucidated. This study derived the equivalent formula to the neural mass model by averaging the network dynamics of neurons. We confirmed that the obtained model reproduces the dynamics of the network model for the spiking neurons through the scaling of the power spectrum density for the population.

Free Research Field

工学数理解析