White noise analysis for the correlation-type elementary motion detectors with half-wave rectifiers. (June 2018)

• Record Type: Journal Article
• Authors: Ikeda, Hideaki; Aonishi, Toru
• Is Part Of: Neural networks. Volume 102(2018)
• Page Start: 96
• Page End: 106
• Publication Date: 2018-06
• Subjects: Motion detection -- Neural coding -- White-noise analysis -- Hassenstein–Reichardt model -- Two-detector model; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.02.014
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: The motion detection mechanism of insects has been attracted attention of many researchers. Several motion-detection models have been proposed on the basis of insect visual system studies. Here, we examine two models, the Hassenstein–Reichardt (HR) model and the two-detector (2D) model. We analytically obtain the mean and variance of the stationary responses of the HR and the 2D models to white noise, and we derive the signal-to-fluctuation-noise ratio (SFNR) to evaluate encoding abilities of the two models. Especially when analyzing the 2D model, we calculate higher-order cumulants of a rectified Gaussian. The results show that the 2D model robustly works almost as well as the HR model in several sets of parameters estimated on the basis of experimental data.