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| Home > Publications database > Focus Session: Collective Dynamics in Neural Networks; Long-Range Collective Dynamics in the Balanced State |
| Home > Publications database > Focus Session: Collective Dynamics in Neural Networks; Long-Range Collective Dynamics in the Balanced State |
| Conference Presentation (Other) | FZJ-2020-00865 |
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2019
Abstract: Experimental findings suggest, that cortical networks operate in a balanced state, in which strong recurrent inhibition suppresses single cell input correlations. The balanced state, however, only restricts the average correlations in the network, the distribution of correlations between individual inputs is not constrained. We here investigate this distribution and establish a functional relation between the distance to criticality and the spatial dependence of the statistics of correlations. Therefore, we develop a mean-field theory that goes beyond self-averaging quantities by taking advantage of the symmetry of the disorder-averaged effective connectivity matrix. We demonstrate that spatially organized, balanced networks can show rich pairwise correlation structures, extending far beyond the range of direct connections. Strikingly, the range of these correlations depends on the distance of the network dynamics to a critical point. This relation between the operational regime of the network and the range of correlations is a potential dynamical mechanism that controls the spatial range on which neurons cooperatively perform computations. In the future we will compare our results with data from multi channel recordings to infer new constraints on realistic network models.
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