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Signal transmission and energy consumption in excitatory–inhibitory cortical neuronal network

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Abstract

Stochastic resonance and energy consumption are significant for information processing and transmission in the neural system. In this paper, we constructed an excitatory–inhibitory cortical neuronal network to investigate the response of the system to weak signals and the corresponding energy consumption. The findings indicate that the excitability of neurons modulates the performance of signal response. Furthermore, the performance of signal response exhibits a bell-shaped dependence on ion channel noise, which is a typical manifestation of the stochastic resonance phenomenon. Stochastic resonance also exists in the network with increasing noise at different excitatory coupling strengths and inhibitory coupling strengths. Furthermore, it is found that the neuronal system obtains optimal transmission of the weak signal at a lower energy consumption. It illustrates that there is a certain economy and efficiency in the signal transmission. At weak inhibitory coupling strength, an optimal excitatory coupling strength exists to allow the neuronal network to make the optimal transmission of the weak signal. However, the phenomenon of double resonant peaks occurs at strong inhibitory coupling strength, which is due to the balance of excitatory and inhibitory synaptic currents. Finally, we demonstrated the robustness of the results to network topology and initial conditions. The results of this paper may contribute to the understanding of signal transmission and its energy consumption in cortical networks.

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 12175080, and also supported by the Fundamental Research Funds for the Central Universities under CCNU22JC009.

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This research was funded by National Natural Science Foundation of China, (Grant no: 12175080).

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Correspondence to Ya Jia.

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Li, X., Yu, D., Li, T. et al. Signal transmission and energy consumption in excitatory–inhibitory cortical neuronal network. Nonlinear Dyn 112, 2933–2948 (2024). https://doi.org/10.1007/s11071-023-09181-4

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