Moritz Helias: Field Theory for Collective Neuronal Statistics
| When |
Apr 10, 2019
from 10:15 AM to 11:00 AM |
|---|---|
| Where | Bernstein Center Freiburg | Hansastr. 9a |
| Contact Name | Stefan Rotter |
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Abstract
Collective phenomena putatively form the basis of information processing in neuronal networks. Activity in such networks is complicated by two interplaying phenomena: fluctuations and non-linearities. Different orders of the statistics are hence coupled.
Similar problems arise in various fields of physics. The common language towards a solution, which crystallized out of
parallel developments over five decades, is (statistical) field theory; it is also applicable to neuronal systems [1].
We here present two methods that employ concepts from field theory to arrive at systematic approximations of such statistics: The perturbation expansion of self-consistency equations around a non-Gaussian solvable problem [2] and the functional renormalizationgroup [3].
We demonstrate the use of these approaches for the inverse problem,determining parameters from given statistics, and we derive effectivedeterministic equations that implicitly capture statistical corrections.
[1] Helias M, Dahmen D (2019)
Statistical field theory for neural networks
arXiv:1901.10416 [cond-mat.dis-nn]
[2] Kuehn T, Helias M (2018)
Expansion of the effective action around non-Gaussian theories
J Phys A: Math Theor 51, 375004
[3] Stapmanns J, Kühn T, Dahmen D, Luu T, Honerkamp C, Helias M
Self-consistent formulations for stochastic nonlinear neuronal dynamics
More about the speaker and his research:
Neuroscience and Medicine | FZ Jülich