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Energy-efficient firing patterns with sparse bursts in the Chay neuron model

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Abstract

The energy efficiency of neural signal transmission is thought to be an important constraint in the nervous system. It is generally measured as the energy consumed per unit of information. Most of the previous studies have demonstrated this efficiency by focusing on single action potentials. However, neural information is more likely to be encoded by a spike train rather than by a single spike. To date, how the energy efficiency is dependent on patterns of spike trains is still unclear. In this study, we examined the energy efficiency of various firing patterns simulated by the Chay neuron model, including relatively high-frequency activities with massive spikes, medium-frequency activities with a moderate number of spikes, and low-frequency activities with rare spikes. Our results indicate that medium-frequency patterns are more energy efficient than both the high-frequency and low-frequency patterns. The most efficient medium-frequency pattern is a sparse burst firing (SBF) pattern because it consumes minimal energy and transmits an amount of neural information comparable to that of high-frequency patterns which consume much more energy. SBF patterns minimize energy consumption not only by producing fewer spikes than high-frequency patterns, but more importantly, also by consuming available energy sources, namely the potential energy stored in ionic concentration gradients, in a balanced way. Furthermore, with fewer spikes, the irregular spike trains of SBF patterns with short bursts and single spikes maximize the neural information that they carry, leading to higher energy efficiency. Thus, the sensory system may give priority to limiting energy costs over maximizing information to achieve greater energy efficiency.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants Nos. 11232005, 11472104, 11872180, and 11972159) and the China Scholarship Council (CSC No. 201706740042). This manuscript was communicated by Professor Wang Rubin at rbwang@163.com during the submission and revision.

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  1. Institute for Cognitive Neurodynamics, School of Science, East China University of Science and Technology, Shanghai, 200237, China

    Fengyun Zhu, Rubin Wang & Xiaochuan Pan

  2. Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan

    Fengyun Zhu & Kazuyuki Aihara

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  1. Fengyun Zhu
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Correspondence to Rubin Wang or Xiaochuan Pan.

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Zhu, F., Wang, R., Aihara, K. et al. Energy-efficient firing patterns with sparse bursts in the Chay neuron model. Nonlinear Dyn 100, 2657–2672 (2020). https://doi.org/10.1007/s11071-020-05593-8

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