title: Self-organizing neural network model of motion processing in the visual cortex during smooth pursuit.
abstract: A physiologically based neural network model was constructed to study cortical motion processing during pursuit eye movements. The model consists of three layers of computational units, simulating information processing by direction selective neurons in the primary visual cortex (V1), motion selective neurons in the middle-temporal area, and pursuit selective neurons in the middle-superior-temporal (MST) area. MST units integrate visual and eye-movement related information, and their connections develop during an unsupervised training process. The resulting MST units represent a transition from retinal to real-world reference frame. By analyzing the model connectivity, mechanisms underlying the functions performed by the network are studied.
authors.affiliation: Department of Biomedical Engineering, Technion, Israel Institute of Technology, 32000, Haifa, Israel.
authors.forename: Moran
authors.initials: M
authors.lastname: Furman
authors.forename: Moshe
authors.initials: M
authors.lastname: Gur
mesh_terms: D001931:Brain Mapping; D005133:Eye Movements / Q000502:physiology; D006801:Humans; D009039:Motion Perception / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods; D011698:Pursuit, Smooth / Q000502:physiology*; D015357:Vision Disparity / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology