Modulatory feedback explain object segmentation by attention

Studies in neuroscience inspired progress in the design of artificial neural networks (ANNs) and vice versa ANNs have also started to provide new insights into the functioning of brain circuits. So far, the focus has been on how ANNs can help to explain the tuning of neurons at various stages of the visual cortical hierarchy. However, the role of modulatory feedback connections, which play a role in attention and perceptual organization, has not been tested yet. Here, we present a biologically plausible neural network that performs scene segmentation and can shift attention using modulatory feedback connections from higher to lower brain areas. The model replicates several neurophysiological signatures of recurrent processing. Specifically, figural regions elicit more neuronal activity in model units than the background. The modulation of neuronal activity by figure and ground occurs at a delay after the first feedforward response, because it depends on a loop through the higher model areas. Importantly, the enhancement of the figural response is controlled by object-based attention, which stays focused on the figural regions and does not spill over to the adjacent background, as observed in the visual cortex. Our results indicate how progress in deep learning can be used to garner insight into the recurrent cortical processing for scene segmentation and object-based attention. ### Competing Interest Statement The authors have declared no competing interest.