Object Boundary Detection in Natural Images May Depend on "Incitatory" Cell-Cell Interactions

• Published in: The Journal of neuroscience Vol. 42; no. 48; pp. 8960 - 8979
• Main Authors: Mel, Gabriel C, Ramachandra, Chaithanya A, Mel, Bartlett W
• Format: Journal Article
• Language: English
• Published: United States Society for Neuroscience 30-11-2022
• Subjects: Bayes Theorem; Bayesian analysis; Cell Communication; Cell interactions; Circuits; Learning; Object recognition; Orientation behavior; Receptive field; Recognition, Psychology; Synaptic strength; Visual Cortex; vision; V1; cortical circuits; excitatory-inhibitory interactions; natural image statistics
• ISSN: 0270-6474; 1529-2401
• DOI: 10.1523/JNEUROSCI.2581-18.2022

Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional "simple cells," we labeled 30,000 natural image patches and used Bayes' rule to help determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified the following three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, and nonmonotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models, we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition-a compound effect we call "incitation"-can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a single-layer "delta" rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems.
Simple cells in primary visual cortex (V1) respond to oriented edges and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell-a divisively normalized linear filter-is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.