The simultaneous coding of heading and path in primate MSTd

The spatio-temporal displacement of luminance patterns in a 2D image is called optic flow. Present biologically-inspired approaches to navigation that use optic flow largely focus on the problem of extracting the instantaneous direction of travel (heading) of a mobile agent. Computational models have demonstrated success in estimating heading in highly constrained environments whereby the agent is largely assumed to travel along straight paths. However, drivers competently steer around curved road bends and humans have been shown capable of judging their future, possibly curved, path of travel in addition to instantaneous heading. The computation of the general future path of travel, which need not be straight, is of interest to mobile robotic, autonomous vehicle driving, and path planning applications, yet no biologically-inspired neural network model exists that provides mechanisms through which the future path may be estimated. We present a biologically inspired recurrent neural network, based on brain area MSTd, that can dynamically code both instantaneous heading and path simultaneously. We show that the model performs similarly to humans in judging heading and the curvature of the future path.