Coupled Networks, Patterns and Complexity - Abstract

Kirst, Christoph

Self-organized and locally controlled information flow in modular neuronal networks

Control of information flow between neurons or groups of neurons is essential in a functional brain, e.g. for context and brain state dependent processing. Reducing neuronal oscillatory dynamics to a phase - amplitude description, we show how alternative phase shifts between different neurons or groups of neurons result in different effective connectivities. We present a theoretical framework to predict the information flow between the oscillators as a function of structural and dynamical network parameter. We use our results to reveal how effective connectivity is controlled by the underlying physical structure and the dynamical state of the network. Interestingly, we find that local changes within a modular subunit of the network, e.g. a change in the strength of a single local link can remotely control the effective connectivity between two different physically unchanged groups of oscillators. We finally link our results to biophysically more realistic networks of spiking neurons.