Neuroscience
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Animal models are a necessary component of systems neuroscience research. Determining which animal model to use for a given study involves a complicated calculus. ⋯ In this review, I discuss work done in my laboratory to investigate the neural mechanisms of color vision in the rhesus macaque. The emphasis is on the strengths of the macaque model, but shortcomings are also discussed.
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Can the human brain itself serve as a model for a systems neuroscience approach to understanding the human brain? After all, how the brain is able to create the richness and complexity of human behavior is still largely mysterious. What better choice to study that complexity than to study it in humans? However, measurements of brain activity typically need to be made non-invasively which puts severe constraints on what can be learned about the internal workings of the brain. ⋯ Resting both on quantitative measurements and considerations of what is known through animal models, we concluded that weighting of sensory signals by the magnitude of their response is a neural mechanism for efficient selection of sensory signals and consequent improvements in behavioral performance with attention. While animal models have many technical advantages over studying the brain in humans, we believe that human systems neuroscience should endeavor to validate, replicate and extend basic knowledge learned from animal model systems and thus form a bridge to understanding how the brain creates the complex and rich cognitive capacities of humans.
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Image segmentation is a fundamental aspect of vision and a critical part of scene understanding. Our visual system rapidly and effortlessly segments scenes into component objects but the underlying neural basis is unknown. ⋯ We found that many neurons tuned to boundary curvature maintained their shape selectivity over a large range of occlusion levels as compared to neurons that are not tuned to boundary curvature. This lends support to the hypothesis that segmentation in the face of occlusion may be solved by contour grouping.
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Human psychophysics is the quantitative measurement of our own perceptions. In essence, it is simply a more sophisticated version of what humans have done since time immemorial: noticed and reflected upon what we can see, hear, and feel. In the 21st century, when hugely powerful techniques are available that enable us to probe the innermost structure and function of nervous systems, is human psychophysics still relevant? I argue that it is, and that in combination with other techniques, it will continue to be a key part of neuroscience for the foreseeable future. I discuss these points in detail using the example of binocular stereopsis, where human psychophysics in combination with physiology and computational vision, has made a substantial contribution.