Neural network model for analyzing optic flow

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 analyze optic flow. The MST cells respond selectively to rotation, expansion/contraction and spiral motion. Previously, it was suggested that vector-field calculus is useful for analyzing optic flow. However, few MST models have been proposed so far, based on it. This paper adopts the vector-field hypothesis and explicitly proposes a novel neural network model for MST. Our model consists of hierarchically connected layers, namely, retina, V1, MT and MST. V1 cells measure local velocity. MT cells extract relative velocity with their antagonistic networks. MST cells simply collect the signals from MT cells and respond selectively to various types of optic flows. We demonstrate through a computer simulation that the simple architecture of our model is enough to explain a variety of results of neurophysiological experiments.