title: Neural network model for extracting optic flow.
abstract: When we travel in an environment, we have an optic flow on the retina. Neurons in the area MST of macaque monkeys are reported to have a very large receptive field and analyze optic flows on the retina. Many MST-cells respond selectively to rotation, expansion/contraction and planar motion of the optic flow. Many of them show position-invariant responses to optic flow, that is, their responses are maintained during the shift of the center of the optic flow. It has long been suggested mathematically that vector-field calculus is useful for analyzing optic flow field. Biologically, plausible neural network models based on this idea, however, have little been proposed so far. This paper, based on vector-field hypothesis, proposes a neural network model for extracting optic flows. Our model consists of hierarchically connected layers: retina, V1, MT and MST. V1-cells measure local velocity. There are two kinds of MT-cell: one is for extracting absolute velocities, the other for extracting relative velocities with their antagonistic inputs. Collecting signals from MT-cells, MST-cells respond selectively to various types of optic flows. We demonstrate through a computer simulation that this simple network is enough to explain a variety of results of neurophysiological experiments.
authors.affiliation: Tokyo University of Technology Hachioji, Tokyo 192-0982, Japan. tmkz@mf.teu.ac.jp
authors.forename: Kazuya
authors.initials: K
authors.lastname: Tohyama
authors.forename: Kunihiko
authors.initials: K
authors.lastname: Fukushima
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D008251:Macaca; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010775:Photic Stimulation; D017603:Signal Detection, Psychological; D013702:Temporal Lobe / Q000502:physiology; D014785:Vision, Ocular / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology*