Abstract
Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.
Highlights
A typical sensory response process in the nervous system consists of the active selection of relevant inputs, the segregation of the different features of the input, and the integration of the information leading to the right action
Collective dynamics in brain networks are characterized by a coordinated activity of their constituent neurons that lead to brain oscillations
Many evidences highlight the role that brain oscillations play in signal transmission, the control of the effective communication between brain areas, and the integration of information processed by different specialized regions
Summary
A typical sensory response process in the nervous system consists of the active selection of relevant inputs, the segregation of the different features of the input, and the integration of the information leading to the right action. All these stages depend on the flexibility in the information routing, as well as in an efficient communication between different regions of the nervous system. One interesting and widespread proposal is that in the presence of neural oscillations, communication patterns can be controlled by adjusting the phase relationship between local oscillations of different brain regions [1,2,3,4,5,6,7,8]. While in the original proposal, the widespread variability in neural systems and the inconsistency of the coherence across time and space were ignored, recent studies showed that the mechanism works if oscillations are not persistent and even if the locking is not stable [19, 20]
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