A recurrent neural model for proto-object based contour integration and figure-ground segregation

AbstractVisual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use ofgrouping neurons whose activity represents tentative objects ( “proto-objects”) based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation lev el biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al.Journal of Neuroscience, 20(17), 6594 –66112000; Qiu et al.Nature Neuroscience, 10(11), 1492 –14992007; Chen et al.Neuron, 82(3), 682 –6942014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.
Source: Journal of Computational Neuroscience - Category: Neuroscience Source Type: research