Abstract

Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the α-band and to the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the α-band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable α-band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing α and δ rhythms during anesthesia, we model the interaction of two excitatory networks with one inhibitory network, showing that this minimal topology underlies the generation of a persistent α-band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the α-band appears when external inputs are suppressed, while fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, α-oscillations could result from the synaptic dynamics of interacting excitatory neuronal networks with and without AHP, a principle that could apply to other rhythms.

Highlights

  • Electroencephalogram (EEG) is used to monitor the brain activity in various conditions such as sleep [1, 2], coma [3] or meditation [4] and to reveal and quantify the presence of multiple frequency oscillations [5] over time [6]

  • We report that the α-band appears during Up states in neuronal populations driven by short-term synaptic plasticity and synaptic noise

  • We show that three connected neuronal networks representing the thalamocortical loop reproduce the dynamics of the α-band, which emerges following the arrest of excitatory stimulations, but that can disappear by increasing inhibitory inputs

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Summary

Introduction

Electroencephalogram (EEG) is used to monitor the brain activity in various conditions such as sleep [1, 2], coma [3] or meditation [4] and to reveal and quantify the presence of multiple frequency oscillations [5] over time [6]. This analysis can be used to assess the level of consciousness or depth of unconsciousness of the brain during anesthesia [7, 8]. This causality between α-band suppression and burst-suppressions remains unexplained

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