title: Effect of the small-world structure on encoding performance in the primary visual cortex: an electrophysiological and modeling analysis.
abstract: The biological networks have been widely reported to present small-world properties. However, the effects of small-world network structure on population's encoding performance remain poorly understood. To address this issue, we applied a small world-based framework to quantify and analyze the response dynamics of cell assemblies recorded from rat primary visual cortex, and further established a population encoding model based on small world-based generalized linear model (SW-GLM). The electrophysiological experimental results show that the small world-based population responses to different topological shapes present significant variation (t test, p < 0.01; effect size: Hedge's g > 0.8), while no significant variation was found for control networks without considering their spatial connectivity (t test, p > 0.05; effect size: Hedge's g < 0.5). Furthermore, the numerical experimental results show that the predicted response under SW-GLM is more accurate and reliable compared to the control model without small-world structure, and the decoding performance is also improved about 10 % by taking the small-world structure into account. The above results suggest the important role of the small-world neural structure in encoding visual information for the neural population by providing electrophysiological and theoretical evidence, respectively. The study helps greatly to well understand the population encoding mechanisms of visual cortex.
authors.affiliation: The School of Electrical Engineering, Zhengzhou University, No. 100 on Kexue Road, Zhengzhou, Henan Province, 450001, China.
authors.forename: Li
authors.initials: L
authors.lastname: Shi
authors.forename: Xiaoke
authors.initials: X
authors.lastname: Niu
authors.forename: Hong
authors.initials: H
authors.lastname: Wan
mesh_terms: D000465:Algorithms; D000818:Animals; D066264:Datasets as Topic; D016014:Linear Models; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D051381:Rats; D020318:Rats, Long-Evans; D014793:Visual Cortex / Q000502:physiology*