%0 Conference Paper
%A Layer, Moritz
%A Dahmen, David
%A Helias, Moritz
%A Deutz, Lukas
%A Voges, Nicole
%A Grün, Sonja
%A Diesmann, Markus
%A Dabrowska, Paulina
%A Papen, Michael von
%T Long-Range Neuronal Coordination Near the Breakdown of Linear Stability
%M FZJ-2020-00869
%D 2019
%X 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.
%B Bernstein Conference 2019
%C 17 Sep 2019 - 20 Sep 2019, Berlin (Germany)
Y2 17 Sep 2019 - 20 Sep 2019
M2 Berlin, Germany
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/873626