title: Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals.
abstract: The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.
authors.affiliation: Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000, China and Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education, Lanzhou University, Lanzhou 730000, China.
authors.forename: Lianchun
authors.initials: L
authors.lastname: Yu
authors.affiliation: College of Electrical Engineering, Northwest University for Nationalities, Lanzhou 730070, China.
authors.forename: Liwei
authors.initials: L
authors.lastname: Liu
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D004735:Energy Transfer / Q000502:physiology*; D006801:Humans; D015640:Ion Channel Gating / Q000502:physiology*; D008564:Membrane Potentials / Q000502:physiology*; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D013269:Stochastic Processes