This title appears in the Scientific Report :
2019
Long-Range Neuronal Coordination Near the Breakdown of Linear Stability
Long-Range Neuronal Coordination Near the Breakdown of Linear Stability
Experimental findings suggest that cortical networks operate in a balanced state [1] in which strong recurrent inhibition suppresses single cell input correlations [2,3]. The balanced state, however, only restricts the average correlations in the network, the distribution of correlations between ind...
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Personal Name(s): | Layer, Moritz (Corresponding author) |
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Dahmen, David / Helias, Moritz / Deutz, Lukas / Voges, Nicole / Grün, Sonja / Diesmann, Markus / Dabrowska, Paulina / Papen, Michael von | |
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: | Bernstein Conference 2019, Berlin (Germany), 2019-09-17 - 2019-09-20 |
Document Type: |
Poster |
Research Program: |
SMARTSTART Training Program in Computational Neuroscience Doktorand ohne besondere Förderung Theory of multi-scale neuronal networks GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration Theory, modelling and simulation |
Publikationsportal JuSER |
Experimental findings suggest that cortical networks operate in a balanced state [1] in which strong recurrent inhibition suppresses single cell input correlations [2,3]. The balanced state, however, only restricts the average correlations in the network, the distribution of correlations between individual neurons is not constrained. We here investigate this distribution and establish a functional relation between the dynamical state of the system and the variance of correlations as a function of cortical distance. The former is characterized by the spectral radius, a measure for how strong a signal is damped while traversing the network. To this end, we develop a theory that captures the heterogeneity of correlations across neurons. Technically, we derive a mean-field theory that assumes the distribution of correlations to be self-averaging; i.e. the same in any realization of the random network. This is possible by taking advantage of the symmetry of the disorder-averaged [4] effective connectivity matrix. We here demonstrate that spatially organized, balanced network models predict rich pairwise correlation structures with spatial extent far beyond the range of direct connections [5]. Massively parallel spike recordings of macaque motor cortex quantitatively confirm this prediction. We show that the range of these correlations depends on the spectral radius, which offers a potential dynamical mechanism to control the spatial range on which neurons cooperatively perform computations. |