Modeling the development of cortical responses in primate dorsal (where) pathway to optic flow using hierarchical neural field models

Abstract

Although there is a plethora of modelling literature dedicated to the object recognition processes of the ventral ("what") pathway of primate visual systems, modelling studies on the motion sensitive regions like the Medial superior temporal area (MST) of the dorsal ("where") pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons in primates. The first two models are each composed of 3 stages: the first stage comprises the Direction Selective Mosaic Network (DSMN), the second stage comprises the Cell Plane Network (CPNW) or the Hebbian Network (HBNW) and the third stage comprises the optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas respectively in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian learning. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained using supervised backpropagation algorithm. We created various dot configurations that can move in translational, radial, and rotational trajectories to make training and test set. The simulation results show that, while neurons in model-1 and model-2 could account for MSTd cell properties found neurobiologically, model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway.