title: Energy-efficient population coding constrains network size of a neuronal array system.
abstract: We consider the open issue of how the energy efficiency of the neural information transmission process, in a general neuronal array, constrains the network size, and how well this network size ensures the reliable transmission of neural information in a noisy environment. By direct mathematical analysis, we have obtained general solutions proving that there exists an optimal number of neurons in the network, where the average coding energy cost (defined as energy consumption divided by mutual information) per neuron passes through a global minimum for both subthreshold and superthreshold signals. With increases in background noise intensity, the optimal neuronal number decreases for subthreshold signals and increases for suprathreshold signals. The existence of an optimal number of neurons in an array network reveals a general rule for population coding that states that the neuronal number should be large enough to ensure reliable information transmission that is robust to the noisy environment but small enough to minimize energy cost.
authors.affiliation: Institute of Theoretical Physics, Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education, Lanzhou University, Lanzhou, 730000, China.|State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100109, China.
authors.forename: Lianchun
authors.initials: L
authors.lastname: Yu
authors.affiliation: Cuiying Honors College, Lanzhou University, Lanzhou, 730000, China.
authors.forename: Chi
authors.initials: C
authors.lastname: Zhang
authors.affiliation: College of Electrical Engineering, Northwest University for Nationalities, Lanzhou, 730070, China.
authors.forename: Liwei
authors.initials: L
authors.lastname: Liu
authors.affiliation: School of Life Science and the Collaborative Innovation Center for Brain Science, Center for Computational Systems Biology, Fudan University, 200433, China.
authors.forename: Yuguo
authors.initials: Y
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission