Mechanisms of Adaptive Spatial Integration in a Neural Model of Cortical Motion Processing
In visual cortex information is processed along a cascade of neural mechanisms that pool activations from the surround with spatially increasing receptive fields. Watching a scenery of multiple moving objects leads to object boundaries on the retina defined by discontinuities in feature domains such as luminance or velocities. Spatial integration across the boundaries mixes distinct sources of input signals and leads to unreliable measurements. Previous work  proposed a luminance-gated motion integration mechanism, which does not account for the presence of discontinuities in other feature domains. Here, we propose a biologically inspired model that utilizes the low and intermediate stages of cortical motion processing, namely V1, MT and MSTl, to detect motion by locally adapting spatial integration fields depending on motion contrast. This mechanism generalizes the concept of bilateral filtering proposed for anisotropic smoothing in image restoration in computer vision.
KeywordsMotion Estimation Neural Modeling Motion Integration Diffusion
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