ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

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Rooy M. ., Novikov N. A., Zakharov D. G., Gutkin B. S. Interaction between PFC neural networks ultraslow fluctuations and brain oscillations. Izvestiya VUZ. Applied Nonlinear Dynamics, 2020, vol. 28, iss. 1, pp. 90-97. DOI: 10.18500/0869-6632-2020-28-1-90-97

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Interaction between PFC neural networks ultraslow fluctuations and brain oscillations

Rooy Marie , National Research University "Higher School of Economics"
Novikov N. A., National Research University "Higher School of Economics"
Zakharov Denis Gennadevich, National Research University "Higher School of Economics"
Gutkin Boris S, National Research University "Higher School of Economics"

Aim of the work was to study the influence of different brain rhythms (i.e. theta, beta, gamma ranges with frequencies from 5 to 80 Hz) on the ultraslow oscillations with frequency of 0.5 Hz and below, where high and low activity states alternate. Ultraslow oscillations are usually observed within neural activity in the human brain and in the prefrontal cortex in particular during rest. Ultraslow oscillations are considered to be generated by local cortical circuitry together with pulse-like inputs and neuronal noise. Structure of ultraslow oscillations shows specific statistics and their characteristics has been connected with cognitive abilities, such as working memory performance and capacity. Methods. In the study we used previously constructed computational model describing activity of a cortical circuit consisting of the populations of pyramidal cells and interneurons. This model was developed to mimic global input impinging on the local prefrontal cortex circuit from other cortical areas or subcortical structures. The model dynamics was studied numerically. Results. We found that frequency increase deferentially lengthens the up states and therefore increases stability of self-sustained activity with oscillations in the gamma band. Discussion. We argue that such effects would be beneficial to information processing and transfer in cortical networks with hierarchical inhibition


Acknowledgements. This work was supported by Russian Science Foundation, grant no. 17-11-01273.


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