The receptive fields of neurons in the mammalian primary visual cortex are oriented not only in the domain of space, but in most cases, also in the domain of space-time. While the orientation of a receptive field in space determines the selectivity of the neuron to image structures at a particular orientation, a receptive field's orientation in space-time characterizes important additional properties such as velocity and direction selectivity. Previous studies have focused on explaining the spatial receptive field properties of visual neurons by relating them to the statistical structure of static natural images. In this report, we examine the possibility that the distinctive spatiotemporal properties of visual cortical neurons can be understood in terms of a statistically efficient strategy for encoding natural time varying images. We describe an artificial neural network that attempts to accurately reconstruct its spatiotemporal input data while simultaneously reducing the statistica...
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