A dynamical model of visual motion processing for arbitrary stimuli including type II plaids. (May 2023)

• Record Type: Journal Article
• Authors: Korai, Yusuke; Miura, Kenichiro
• Is Part Of: Neural networks. Volume 162(2023)
• Page Start: 46
• Page End: 68
• Publication Date: 2023-05
• Subjects: Computational neuroscience -- Dynamical model -- Optimization -- Motion perception -- Eye movement; 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.2023.02.039
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: To explore the operating principle of visual motion processing in the brain underlying perception and eye movements, we model the information processing of velocity estimate of the visual stimulus at the algorithmic level using the dynamical system approach. In this study, we formulate the model as an optimization process of an appropriately defined objective function. The model is applicable to arbitrary visual stimuli. We find that our theoretical predictions qualitatively agree with time evolution of eye movement reported by previous works across various types of stimulus. Our results suggest that the brain implements the present framework as the internal model of motion vision. We anticipate our model to be a promising building block for more profound understanding of visual motion processing as well as for the development of robotics. Highlights: Visual motion processing is modelled as an optimization process. The model is applicable to arbitrary visual stimuli. Theoretical predictions agree with the observed eye movements. The model provides verifiable predictions.