EEG Autocorrelation Analysis of Neuronal Population at Criticality

Zhen Wu; Xiao Fei Xia; Jun Song Wang

doi:10.4028/www.scientific.net/AMM.482.363

Paper Titles

Performance Evaluation of Smart Grid Based on Optimal Fuzzy Algorithm Method
p.346

Modeling and Simulation on Unidirectional Pedestrian Flow Based on Cellular Automata
p.350

A Model of Point Multiresolution Rendering Method
p.355

CFD Analysis of Shrouded Diameter in Aerosol Sampling Inlet
p.359

EEG Autocorrelation Analysis of Neuronal Population at Criticality
p.363

MEMS/GPS Integrated Navigation System Based on EKF
p.367

Re-Entrant Jet Analysis of Cavitating Turbulent Flow on a Hydrofoil
p.375

A Simulated Study on the Combustion and Emission Performances of Diesel Engines
p.381

Design and Test of High Speed Digitization Sampling Circuit Based on FPGA
p.386

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 482EEG Autocorrelation Analysis of Neuronal...

EEG Autocorrelation Analysis of Neuronal Population at Criticality

Abstract:

The brain operates at criticality not only at resting state but also with some recognition tasks. Researches have shown that the information transmission efficiency is maximized when brain operates at criticality, however, the underlined mechanism remains some unknown. In this study, we elucidate the underlined mechanism of neuronal information transmission at criticality through a computational study. Firstly, a bifurcation analysis is conducted, by which we can obtain the Hopf bifurcation curve, responding the critical state. Secondly, we compute the autocorrelation function of the EEG (Electroencephalography) signals. The results have demonstrated that the autocorrelation function at criticality decay slowly and is much larger than other states, meaning long range dependence of the EEG signals at criticality, which reveals that the large autocorrelation function results that the information transmission efficiency is maximized at criticality.

You might also be interested in these eBooks

Structural Engineering, Vibration and Aerospace Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 482)

Pages:

363-366

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.482.363

Citation:

Cite this paper

Online since:

December 2013

Authors:

Zhen Wu, Xiao Fei Xia, Jun Song Wang

Keywords:

Autocorrelation, Criticality, EEG, Neuronal Population

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] D. R. Chialvo, Emergent complex neural dynamics, Nature Physics, vol. 6 (2010), p.744.

Google Scholar

[2] T. Petermann, T. C. Thiagarajan, M. A. Lebedev, M. A. Nicolelis, D. R. Chialvo, and D. Plenz, Spontaneous cortical activity in awake monkeys composed of neuronal avalanches, Proceedings of the National Academy of Sciences, vol. 106 (2009).

DOI: 10.1073/pnas.0904089106

Google Scholar

[3] W. L. Shew, H. Yang, S. Yu, R. Roy, and D. Plenz, Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches, The Journal of Neuroscience, vol. 31 (2011), p.55.

DOI: 10.1523/jneurosci.4637-10.2011

Google Scholar

[4] Jansen BH, Rit VG, Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns, Biol Cybern, vol. 73 (1995), p.357.

DOI: 10.1007/bf00199471

Google Scholar

[5] M. J. Aburn, C. Holmes, J. A. Roberts, T. W. Boonstra, and M. Breakspear, Critical fluctuations in cortical models near instability, Frontiers in physiology, vol. 3 (2012).

DOI: 10.3389/fphys.2012.00331

Google Scholar