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Motion Processing in the Macaque: Revisited with Functional Magnetic Resonance Imaging

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Tolias,  AS
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Smirnakis,  SM
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Augath,  MA
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Trinath,  T
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Tolias, A., Smirnakis, S., Augath, M., Trinath, T., & Logothetis, N. (2001). Motion Processing in the Macaque: Revisited with Functional Magnetic Resonance Imaging. The Journal of Neuroscience, 21(21), 8594-8601. doi:10.1523/JNEUROSCI.21-21-08594.2001.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E1AF-8
Abstract
A great deal is known about the response properties of single neurons processing sensory information. In contrast, less is understood about the collective characteristics of networks of neurons that may underlie sensory capacities of animals. We used functional magnetic resonance imaging to study the emergent properties of populations of neurons processing motion across different brain areas. Using a visual adaptation paradigm, we localized a distributed network of visual areas that process information about the direction of motion as expected from single-cell recording studies. However, we found an apparent discrepancy between the directional signals in certain visual areas as measured with blood oxygenation level-dependent imaging compared with an estimate based on the spiking of single neurons. We propose a hypothesis that may account for this difference based on the postulate that neuronal selectivity is a function of the state of adaptation. Consequently, neurons classically thought to lack information about certain attributes of the visual scene may nevertheless receive and process this information. We further hypothesize that this adaptation-dependent selectivity may arise from intra- or inter-area cellular connections, such as feedback from higher areas. This network property may be a universal principle the computational goal of which is to enhance the ability of neurons in earlier visual areas to adapt to statistical regularities of the input and therefore increase their sensitivity to detect changes along these stimulus dimensions.