This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/22680 in citations.
Functional Renormalization Group for Stochastic Rate Neurons
Functional Renormalization Group for Stochastic Rate Neurons
It is often suggested that the cortex operates close to a critical point at which linear response theory fails since the neural dynamics is dominated by large fluctuations on all length scales. The functional Renormalization Group (fRG) is not stained with this flaw because in principle it treats st...
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Personal Name(s): | Kühn, Tobias (Corresponding author) |
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Stapmanns, Jonas / Dahmen, David / Honerkamp, Carsten / Helias, Moritz | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Computational and Systems Neuroscience; IAS-6 Jara-Institut Brain structure-function relationships; INM-10 |
Imprint: |
2019
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Conference: | DPG Spring Meeting, Section Condensed Matter, Regensburg (Germany), 2019-03-31 - 2019-04-05 |
Document Type: |
Conference Presentation |
Research Program: |
Human Brain Project Specific Grant Agreement 1 Theory of multi-scale neuronal networks Supercomputing and Modelling for the Human Brain Connectivity and Activity Theory, modelling and simulation Doktorand ohne besondere Förderung Human Brain Project Specific Grant Agreement 2 |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
It is often suggested that the cortex operates close to a critical point at which linear response theory fails since the neural dynamics is dominated by large fluctuations on all length scales. The functional Renormalization Group (fRG) is not stained with this flaw because in principle it treats statistics of arbitrary order in an unbiased and self-consistent way. We apply fRG to a self-interacting, stochastic, quadratic rate neuron and show how this method incorporates corrections to the mean dynamics and time-dependent statistics due to fluctuations in the presence of nonlinear neuronal gain. To obtain a simplified treatment of the frequency-dependence of all observables, we adapt the Blaizot Méndez-Galain Wschebor (BMW) scheme to the vertex expansion, which yields good predictions.We expect that the insights into fRG-techniques gained within our study will help to tackle challenges occurring in the description of phenomena in spatially extended networks, notably the calculation of critical exponents and the coarse-graining of microscopic models. |