Resting state neural networks and energy metabolism

Noack, Raymond and Manjesh, Chetan and Ruszinkó, Miklós and Siegelmann, Hava and Kozma, Róbert (2017) Resting state neural networks and energy metabolism. In: 2017 International Joint Conference on Neural Networks, IJCNN 2017. Proceedings of the International Joint Conference on Neural Networks (2017-M). IEEE Neural Networks Society, Piscataway (NJ), pp. 228-235. ISBN 9781509061815

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Abstract

The human brain is an energy hungry organ. How that brain manages its energy consumption in maintaining its health and executing sensori-motor and cognitive functions is an important but overlooked research area in contemporary cognitive neuroscience. It is argued here that the principal method whereby the human brain manages its energy utilization is through maintaining a relatively elevated level of activity in what can be referred to as 'resting state networks' (RSN). The elevated energy consumption in the human brain's varied RSNs is driven and maintained by a physiological mechanism we call the Frame-Formation Energy Cycle (FFEC). Running the FFEC cycle is metabolically expensive and therefore offers a mechanism to explain the increased energy consumption in human-brain RSNs as compared to regions not involved in such networks. © 2017 IEEE.

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