Journal of Mathematical Biology

, Volume 66, Issue 7, pp 1475–1497 | Cite as

Neural field theory with variance dynamics

Article
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Abstract

Previous neural field models have mostly been concerned with prediction of mean neural activity and with second order quantities such as its variance, but without feedback of second order quantities on the dynamics. Here the effects of feedback of the variance on the steady states and adiabatic dynamics of neural systems are calculated using linear neural field theory to estimate the neural voltage variance, then including this quantity in the total variance parameter of the nonlinear firing rate-voltage response function, and thus into determination of the fixed points and the variance itself. The general results further clarify the limits of validity of approaches with and without inclusion of variance dynamics. Specific applications show that stability against a saddle-node bifurcation is reduced in a purely cortical system, but can be either increased or decreased in the corticothalamic case, depending on the initial state. Estimates of critical variance scalings near saddle-node bifurcation are also found, including physiologically based normalizations and new scalings for mean firing rate and the position of the bifurcation.

Keywords

Neural field theory Networks Brain dynamics 

Mathematics Subject Classification

92C42 92C20 92B25 92C05 
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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.School of PhysicsUniversity of SydneySydneyAustralia
  2. 2.Brain Dynamics CenterSydney Medical School, Western, University of SydneyWestmeadAustralia

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