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Computations by networks of neurons; fMRI adaptation studies in
monkeys
Andreas S. Tolias, Stelios. M. Smirnakis, Marc Augath, Torsten
Trinath and Nikos K. Logothetis
Max Planck Institute for Biological Cybernetics
Tuebingen, Germany
A great deal is understood about the properties of single neurons
processing visual information. In contrast, less is known about the
collective characteristics of networks of cells that may underlie
sensory capacities of animals. By measuring the blood oxygenation
level-dependent (BOLD) signal in the macaque cortex we studied the
emergent properties of populations of neurons processing motion across
different brain areas. We used a visual adaptation paradigm to
localize a distributed network of visual areas which process
information about direction of motion, and also studied the dynamics
of adaptation of the bold signal elicited by moving stimuli. We found
that the BOLD signal in areas MT and V2/V3 adapted faster than in V1
reflecting the difference in motion processing between these areas.
Moreover, the strength of the directionally selective bold signal in
V1 was much greater than the one estimated on the basis of established
facts from single cell electrophysiology. We propose an hypothesis
that may account for this difference based on the postulate that
neuronal selectivity is a function of the state of adaptation and
therefore neurons classically thought to lack information about
certain attributes of the visual scene, may nevertheless receive and
process this information. The implementation of this hypothesis can
arise as a result of intra- and inter-area cellular connections, such
as feedback from higher areas. This network property may be a
universal principle whose computational goal 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.