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Proc Natl Acad Sci U S A. 2017 May 16;114(20):E4085-E4094. doi: 10.1073/pnas.1619857114. Epub 2017 May 1.

A normalization model suggests that attention changes the weighting of inputs between visual areas.

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Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213
Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213.


Models of divisive normalization can explain the trial-averaged responses of neurons in sensory, association, and motor areas under a wide range of conditions, including how visual attention changes the gains of neurons in visual cortex. Attention, like other modulatory processes, is also associated with changes in the extent to which pairs of neurons share trial-to-trial variability. We showed recently that in addition to decreasing correlations between similarly tuned neurons within the same visual area, attention increases correlations between neurons in primary visual cortex (V1) and the middle temporal area (MT) and that an extension of a classic normalization model can account for this correlation increase. One of the benefits of having a descriptive model that can account for many physiological observations is that it can be used to probe the mechanisms underlying processes such as attention. Here, we use electrical microstimulation in V1 paired with recording in MT to provide causal evidence that the relationship between V1 and MT activity is nonlinear and is well described by divisive normalization. We then use the normalization model and recording and microstimulation experiments to show that the attention dependence of V1-MT correlations is better explained by a mechanism in which attention changes the weights of connections between V1 and MT than by a mechanism that modulates responses in either area. Our study shows that normalization can explain interactions between neurons in different areas and provides a framework for using multiarea recording and stimulation to probe the neural mechanisms underlying neuronal computations.


attention; normalization; variability

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