Interest in the processing of optic flow has increased recently in both the neurophysiological and the psychophysical communities. We have designed a neural network model of the visual motion pathway in higher mammals that detects the direction of heading from optic flow. The model is a neural implementation of the subspace algorithm introduced by Heeger and Jepson (1990). We have tested the network in simulations that are closely related to psychophysical and neurophysiological experiments and show that our results are consistent with recent data from both fields. The network reproduces some key properties of human ego-motion perception. At the same time, it produces neurons that are selective for different components of ego-motion flow fields, such as expansions and rotations. These properties are reminiscent of a subclass of neurons in cortical area MSTd, the triple-component neurons. We propose that the output of such neurons could be used to generate a computational map of heading directions in or beyond MST.
2D LiDAR Map Prediction via Estimating Motion Flow with GRU
