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Many details are known about microcircuitry in visual cortices. For example, neurons have supralinear activation functions, they're either excitatory (E) or inhibitory (I), connection strengths fall off with distance, and the output cells of an area are excitatory. This circuitry is important as it's believed to support core functions such as normalization and surround suppression. Yet, multi-area models of the visual processing stream don't usually include these details. Here, we introduce known-features of recurrent processing into the architecture of a convolutional neural network and observe how connectivity and activity change as a result. We find that certain E-I differences found in data emerge in the models, though the details depend on which architectural elements are included. We also compare the representations learned by these models to data, and perform analyses on the learned weight structures to assess the nature of the neural interactions. 

Imagine you have lost your cell phone. Your eyes scan the cluttered table in front of you, searching for its familiar blue case. But what is happening within the visual areas of your brain while you search? One possibility is that neurons that represent relevant features such as 'blue' and 'rectangular' increase their activity. This might help you spot your phone among all the other objects on the table. Paying attention to specific features improves our performance on visual tasks that require detecting those features. The 'feature similarity gain model' proposes that this is because attention increases the activity of neurons sensitive to specific target features, such as ‘blue’ in the example above. But is this how the brain solves such challenges in practice? Previous studies examining this issue have relied on correlations. They have shown that increases in neural activity correlate with improved performance on visual tasks. But correlation does not imply causation. Lindsay and Miller have now used a computer model of the brain’s visual pathway to examine whether changes in neural activity cause improved performance. The model was trained to use feature similarity gain to detect an object within a set of photographs. As predicted, changes in activity like those that occur in the brain did indeed improve the model’s performance. Moreover, activity changes at later stages of the model's processing pathway produced bigger improvements than activity changes earlier in the pathway. This may explain why attention affects neural activity more at later stages in the visual pathway. But feature similarity gain is not the only possible explanation for the results. Lindsay and Miller show that another pattern of activity change also enhanced the model’s performance, and propose an experiment to distinguish between the two possibilities. Overall, these findings increase our understanding of how the brain processes sensory information. Work is ongoing to teach computers to process images as efficiently as the human visual system. The computer model used in this study is similar to those used in state-of-the-art computer vision. These findings could thus help advance artificial sensory processing too.