Long-range cortical connections give rise to a robust velocity map of V1
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This paper proposes a two-dimensional velocity model (2DVM) of the primary visual cortex (V1). The model’s novel aspect is that it specifies a particular pattern of long-range cortical temporal connections, via the Connection Algorithm, and shows how the addition of these connections to well-known spatial properties of V1 transforms V1 into a velocity map. The map implies a number of organizational properties of V1: (1) the singularity of each orientation pinwheel contributes to the detection of slow-moving spots across the visual field; (2) the speed component of neuronal velocity selectivity decreases monotonically across each joint orientation contour line for parallel motion and increases monotonically for orthogonal motion; (3) the cells that are direction selective to slow-moving objects are situated in the periphery of V1; and (4) neurons in distinct pinwheels tend to be connected to neurons with similar tuning preferences in other pinwheels. The model accounts for various types of known illusionary perceptions of human vision: perceptual filling-in, illusionary orientation and visual crowding. The three distinguishing features of 2DVM are: (1) it unifies the functional properties of the conventional energy model of V1; (2) it directly relates the functional properties to the known structure of the upper layers of V1; and (3) it implies that the spatial selectivity features of V1 are side effects of its more important role as a velocity map of the visual field.
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Animal Physiology - Systems
Neural, Evolutionary and Fuzzy Computation