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Neural energy mechanism and neurodynamics of memory transformation

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Abstract

This study surveys the interaction between working memory and long-term memory using the neural energy coding method based on a working memory model with \(\hbox {Ca}^{2+}\) subsystem-induced bi-stability. We apply theta-burst stimulation (TBS) and high-frequency stimulation (HFS) to the bi-stable dynamical model to induce long-term memory. During studying the formation of long-term memory, we develop methods to measure the changes in energy input of these two stimuli and the corresponding energy consumption of the memory system. We investigate the minimum energy cost of these two stimuli to induce long-term memory and define an energy ratio to quantitatively describe the energy efficiency of the stimulus. We found that both stimuli induce the similar long-term effects by the dynamical model, but TBS is more energy efficient than HFS. The results provide a more comprehensive understanding in the transformation from working memory, by an energy coding approach, to long-term memory in response to the two types of long-term potentiation-induced protocols, which reflect the physiological mechanisms and neurodynamics of long-term memory generation, and also reveal the energy-efficient principle of the neural system.

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Acknowledgements

The authors thank the editor and reviewers for helpful criticisms and comments. This work is supported by the National Natural Science Foundation of China (Nos. 11802095, 11702096, 11232005, 11472104, 11872180) and the Fundamental Research Funds for the Central Universities of China (Nos. 222201814025 & 222201714020).

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  1. Institute for Cognitive Neurodynamics, School of Science, East China University of Science and Technology, Shanghai, China

    Yihong Wang, Xuying Xu, Yating Zhu & Rubin Wang

  2. School of Computer Science, Hangzhou Dianzi University, Hangzhou, China

    Rubin Wang

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  1. Yihong Wang
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Correspondence to Rubin Wang.

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Wang, Y., Xu, X., Zhu, Y. et al. Neural energy mechanism and neurodynamics of memory transformation. Nonlinear Dyn 97, 697–714 (2019). https://doi.org/10.1007/s11071-019-05007-4

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