Recurrent neural circuits for perceptual grouping

Abstract

Neurons in the visual cortex are sensitive to context: Responses to stimuli presented within their classical receptive fields (CRFs) are modulated by stimuli in their surrounding extra-classical receptive fields (eCRFs). However, the circuits underlying these contextual effects are not well understood, and little is known about how these circuits drive perception during everyday vision. We tackle these questions by approximating circuit-level eCRF models with a differentiable discrete-time recurrent neural network that is trainable with gradient-descent. After optimizing model synaptic connectivity and dynamics for object contour detection in natural images, the neural-circuit model rivals human observers on the task with far better sample efficiency than state-of-the-art computer vision approaches. Notably, the model also exhibits CRF and eCRF phenomena typically associated with primate vision. The model’s ability to accurately detect object contours also critically depends on these effects, and these contextual effects are not found in ablated versions of the model. Finally, we derive testable predictions about the neural mechanisms responsible for contextual integration and illustrate their importance for accurate and efficient perceptual grouping.