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| Home > Publications database > Long-range coordination patterns in cortex change with behavioral context |
| Poster (After Call) | FZJ-2021-00211 |
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2021
Please use a persistent id in citations: http://hdl.handle.net/2128/26879
Abstract: The cerebral cortex is a network of subnetworks that is organized onvarious spatial scales. Understanding how neurons communicate at thedifferent scales is crucial for understanding brain dynamics andfunction. On the microscopic scale the connectivity stems mostly fromlocal axonal arborizations, suggesting coordination is strongestbetween nearby neurons in the range of a few hundred micrometers. Yetrecent studies found activity of neurons across much larger distancesto be organized in manifolds. The emergence of such manifolds relieson complex coordination patterns between neurons. We here analyzemulti-electrode recordings of resting-state activity in macaque motorcortex that indeed show strong positive and negative spike-countcovariances between neurons that are millimeters apart. To understandthe origin of such coordination we develop a conceptually novelnetwork theory that combines the spatial extent and heterogeneity ofthe connectivity with fluctuations of activity treated beyond themean-field approximation. This quantitative theory uncovers a simpleand ubiquitous mechanism that generates long-range correlationpatterns despite short-range connections: the heterogeneity ofconnections causes a dynamical network state that emphasizescooperation of neurons by multi-synaptic interactions. The mechanismdoes not rely on specific connectivity structures, but emerges inspatially organized networks with even random connectivity. The theorynot only explains the experimentally observed shallow exponentialdecay of the width of the covariance distribution at long distances,but also predicts that neuronal coordination patterns can change in astate-dependent manner. We confirm this prediction by comparingactivity in macaque motor cortex across different behavioral epochs ofa reach-to-grasp experiment. Our results explain how spatiallyextended neural manifolds can emerge from the local networkconnectivity.
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