Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image-and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

The timing of action potentials relative to sensory stimuli can be precise down to milliseconds in the visual system, even though the relevant timescales of natural vision are much slower. The existence of such precision contributes to a fundamental debate over the basis of the neural code and, specifically, what timescales are important for neural computation. Using recordings in the lateral geniculate nucleus, here we demonstrate that the relevant timescale of neuronal spike trains depends on the frequency content of the visual stimulus, and that 'relative', not absolute, precision is maintained both during spatially uniform white-noise visual stimuli and naturalistic movies. Using information-theoretic techniques, we demonstrate a clear role of relative precision, and show that the experimentally observed temporal structure in the neuronal response is necessary to represent accurately the more slowly changing visual world. By establishing a functional role of precision, we link visual neuron function on slow timescales to temporal structure in the response at faster timescales, and uncover a straightforward purpose of fine-timescale features of neuronal spike trains.

The hippocampus is essential for learning complex spatial relationships, but little is known about how hippocampal neural activity changes as animals learn about a novel environment. We studied the formation of new place representations in rats by examining the changes in place-specific firing of neurons in the CA1 region of the hippocampus and the relationship between these changes and behavioral change across multiple days of exposure to novel places. We found that many neurons showed very rapid changes on the first day of exposure to the novel place, including many cases in which a previously silent neuron developed a place field over the course of a single pass through the environment. Across the population, the largest changes in neural activity occurred on day 2 of exposure to a novel place, but only if the animal had little experience (Ͻ4 min) in that location on day 1. Longer exposures on day 1 were associated with smaller changes on day 2, suggesting that hippocampal neurons required 5-6 min of experience to form a stable spatial representation. Even after the representation stabilized, the animals' behavior remained different in the novel places, suggesting that other brain regions continued to distinguish novel from familiar locations. These results show that the hippocampus can form new spatial representations quickly but that stable hippocampal representations are not sufficient for a place to be treated as familiar.

The term 'visual adaptation' describes the processes by which the visual system alters its operating properties in response to changes in the environment. These continual adjustments in sensory processing are diagnostic as to the computational principles underlying the neural coding of information and can have profound consequences for our perceptual experience. New physiological and psychophysical data, along with emerging statistical and computational models, make this an opportune time to bring together experimental and theoretical perspectives. Here, we discuss functional ideas about adaptation in the light of recent data and identify exciting directions for future research.

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