Neurally Encoding Time for Olfactory Navigation

Accurately encoding time is one of the fundamental challenges faced by the nervous system in mediating behavior. We recently reported that some animals have a specialized population of rhythmically active neurons in their olfactory organs with the potential to peripherally encode temporal information about odor encounters. If these neurons do indeed encode the timing of odor arrivals, it should be possible to demonstrate that this capacity has some functional significance. Here we show how this sensory input can profoundly influence an animal’s ability to locate the source of odor cues in realistic turbulent environments—a common task faced by species that rely on olfactory cues for navigation. Using detailed data from a turbulent plume created in the laboratory, we reconstruct the spatiotemporal behavior of a real odor field. We use recurrence theory to show that information about position relative to the source of the odor plume is embedded in the timing between odor pulses. Then, using a parameterized computational model, we show how an animal can use populations of rhythmically active neurons to capture and encode this temporal information in real time, and use it to efficiently navigate to an odor source. Our results demonstrate that the capacity to accurately encode temporal information about sensory cues may be crucial for efficient olfactory navigation. More generally, our results suggest a mechanism for extracting and encoding temporal information from the sensory environment that could have broad utility for neural information processing.Author Summary: Many animals navigate turbulent environments using odor cues, a behavior known as olfactory search. We propose a neural mechanism for olfactory search based on evidence that a functional subset of olfactory receptor neurons (ORNs) called bursting ORNs or bORNs can encode the time intervals between successive encounters with odor. We show that these time intervals are estimators of the recurrence time, an information-rich statistic of the turbulent flow. Using a computational model parameterized with data from an actual turbulent plume, we demonstrate that a searcher can locate an odor source efficiently using only input from bORNs. These findings provide scientific evidence that the most important navigational information captured by the olfactory system may come in the form of measurements of time.