A network model for the optic flow computation of the MST neurons

Abstract A simple and biologically plausible model is proposed to simulate the optic flow computation taking place in the medial superior temporal (MST) area of the visual cortex in the primates' brain. The model is a neural network composed of multiple competitive learning layers. The input layer of the network simulates the MT neurons that selectively respond to the visual stimuli with different local velocities and send strong synaptic projections to the MST neurons. The middle and output layers of the network simulate the MST neurons which selectively respond to different types of optic flow motion patterns (planar, circular, radial and spiral motions), as well as different locations of the center of motion (COM). The simulation results show that the behaviours of the output nodes of the networks resemble closely the known responsive properties of the MST neurons found neurophysiologically, such as the existence of three types of MST neurons which respond, respectively, to one, two, or three types of input motion patterns, and the response selectivity for the location of COM. Experimental results with real image data show that the network model is able to detect the location of the COM in the optic flow field.

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