Habituation as a neural algorithm for online odor discrimination

Significance Identifying relevant information embedded within a constant and noisy background is a challenge faced by many sensory systems in the brain. Habituation—the reduction in responsiveness to repeated, neutral stimuli—is an unsupervised learning method the brain uses to overcome this challenge. Here, we propose a neural algorithm for odor habituation based on the olfactory circuit in fruit flies. We demonstrate how habituation enhances discrimination between similar odors and improves foreground detection by subtracting background noise. More broadly, this perspective helps bridge the gap between sensory neurobiology and cognition. Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.