A neural network model of spiral-planar motion tuning in MSTd

Neurophysiological studies in MSTd report the existence of motion pattern selective cells whose visual motion properties span a continuum of values, suggesting a role in estimates of self-motion from optic flow. Biologically motivated models of heading estimation support this view, having identified similar visual motion properties within their "neural" structures. While such models have addressed the computational sufficiency of their respective feed-forward designs they have not explicitly examined the underlying computational structures, particularly as they relate to the interaction between planar and spiral motion responses within MSTd. Here we use an expanded stimulus training set that includes planar motions to extend the range of neurophysiological properties identified within an existing network structure [Network: Comput. Neural Syst. 9 (1998) 467]. In doing so, we quantify the emergent planar motion properties within the network hidden layer and examine how they interact, functionally and computationally, with cardinal/spiral motion pattern responses. Throughout the hidden layer we demonstrate that the input activation associated with a unit's preferred planar motion is consistent with an overlapping gradient hypothesis [J. Neurophysiol. 65(6) (1991) 1346]. Together with the change to a peripheral excitation profile in the presence of a unit's preferred spiral motion these results suggest a more complex computational architecture in which the cell's 'classical' receptive field properties are dependent on the type of stimulus used to map them. Based on the computational model we propose an experimental paradigm to investigate the existence of equivalent computational structures in MSTd.