title: Super resolution: another computational role of short-range horizontal connection in the primary visual cortex.
abstract: Recent physiological data related to the primary visual cortex (V1) have shown various contextual effects in the non-classical receptive field (nCRF). Contextual modulation, size tuning and altered sensitivity of orientation are typical examples of such contextual effects in the nCRF. These phenomena in the nCRF have been thought to be caused by short-range horizontal connection (SHC). However, SHC does not necessarily contribute only to these phenomena. These phenomena might be merely secondary phenomena by the fundamental role of SHC. In this paper, we specifically address the overcomplete properties in V1. Then the fundamental role of SHC is examined from image-processing points of view. Super resolution is proposed as a strong candidate for the fundamental role of SHC. Super resolution is an engineering method that obtains a high-resolution image from a low-resolution image(s). The distribution of SHC is deductively derived by adopting a reverse diffusion technique, which is one of various available super-resolution techniques. The spatial distribution of our proposed SHC is isotropic on the orientation map. This characteristic is consistent with physiological data. In addition to that, contextual modulation, size tuning and altered sensitivity of orientation in numerical experiments using our proposed SHC can be reproduced qualitatively. These results indicate that these phenomena are secondary phenomena by super-resolution processing.
authors.affiliation: Research Institute of Electrical Communication, Tohoku University, Japan. sasaki@riec.tohoku.ac.jp
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Sasaki
authors.forename: Shunji
authors.initials: S
authors.lastname: Satoh
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009949:Orientation / Q000502:physiology; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*