Abstract
Brief bursts of high-frequency spikes are a common firing pattern of neurons. The cellular mechanisms of bursting and its biological significance remain a matter of debate. Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting. The flow of ions across the membrane of the bursting neuron with or without current stimulation and its power which contributes to the change of the transmembrane electrical potential energy are analyzed here in detail. We find that during the depolarization of spikes in bursting this power becomes negative, which was also discovered in previous research with another energy model. We also find that the neuron’s energy consumption during bursting is minimal. Especially in the spontaneous state without stimulation, the total energy consumption (2.152 × 10−7 J) during 30 s of bursting is very similar to the biological energy consumption (2.468 × 10−7 J) during the generation of a single action potential, as shown in Wang et al. (Neural Plast 2017, 2017a). Our results suggest that this property of low energy consumption could simply be the consequence of the biophysics of generating bursts, which is consistent with the principle of energy minimization. Our results also imply that neural energy plays a critical role in neural coding, which opens a new avenue for research of a central challenge facing neuroscience today.
Highlights
Understanding neural coding remains a central focus of neuroscience since the pioneering research of Edgar Adrian in the 1930s (Adrian 1932)
Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting
Recent research has achieved important results with applying various neural models and energy calculation methods (Zheng and Wang 2012; Zheng et al 2014, 2016, 2017a, b). (I) Using the concepts of minimum mutual information and maximum entropy to study neural coding shows that the neural information processing of the brain follows the principles of minimization of energy consumption and maximization of information transmission efficiency (Zheng and Wang 2012; Laughlin and Sejnowski 2003). (II) The calculations from the energy model in Zheng et al (2014), demonstrate that during action potentials, neurons first release stored energy very rapidly and receive from oxyhemoglobins the energy required for subsequent action potentials. (III) Wang et al (2017a) based on the Hodgkin–Huxley model
Summary
Understanding neural coding remains a central focus of neuroscience since the pioneering research of Edgar Adrian in the 1930s (Adrian 1932). A variety of proposed neural coding patterns, such as rate coding, time coding, phase coding and population coding (Optican and Richmond 1987; Lee et al 1988; Igarashi et al 2007; Cessac et al 2010), are applicable only to local neural activity research, and have limited use for the exploration of global brain activity (Butts et al 2007; Gong et al 2010; Guo and Li 2012) To overcome this limitation, researchers have developed an energy coding theory (Wang et al 2006, 2014, 2015a; Wang and Wang 2014; Wang and Zhu 2016) based on the principle that neural activities are energy-expensive coding processes (Hyder et al 2013; Attwell and Laughlin 2001; Torrealdea et al 2009; Alle et al 2009). Recent research has achieved important results with applying various neural models and energy calculation methods (Zheng and Wang 2012; Zheng et al 2014, 2016, 2017a, b). (I) Using the concepts of minimum mutual information and maximum entropy to study neural coding shows that the neural information processing of the brain follows the principles of minimization of energy consumption and maximization of information transmission efficiency (Zheng and Wang 2012; Laughlin and Sejnowski 2003). (II) The calculations from the energy model in Zheng et al (2014), demonstrate that during action potentials, neurons first release stored energy very rapidly and receive from oxyhemoglobins the energy required for subsequent action potentials. (III) Wang et al (2017a) based on the Hodgkin–Huxley model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
