Algorithms for Olfactory Search across Species

Localizing the sources of stimuli is essential. Most organisms cannot eat, mate, or escape without knowing where the relevant stimuli originate. For many, if not most, animals, olfaction plays an essential role in search. While microorganismal chemotaxis is relatively well understood, in larger animals the algorithms and mechanisms of olfactory search remain mysterious. In this symposium, we will present recent advances in our understanding of olfactory search in flies and rodents. Despite their different sizes and behaviors, both species must solve similar problems, including meeting the challenges of turbulent airflow, sampling the environment to optimize olfactory information, and incorporating odor information into broader navigational systems

Natural search algorithms as a bridge between organisms, evolution, and ecology

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reprod uce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology. Keywords: sensing; navigation; evolutionary strategy; encounter rates; exploration–exploitationGordon and Betty Moore Foundation (Award GBMF3783

Odors: from chemical structures to gaseous plumes

We are immersed within an odorous sea of chemical currents that we parse into individual odors with complex structures. Odors have been posited as determined by the structural relation between the molecules that compose the chemical compounds and their interactions with the receptor site. But, naturally occurring smells are parsed from gaseous odor plumes. To give a comprehensive account of the nature of odors the chemosciences must account for these large distributed entities as well. We offer a focused review of what is known about the perception of odor plumes for olfactory navigation and tracking, which we then connect to what is known about the role odorants play as properties of the plume in determining odor identity with respect to odor quality. We end by motivating our central claim that more research needs to be conducted on the role that odorants play within the odor plume in determining odor identity

Design and Performance Evaluation of an Infotaxis-Based Three-Dimensional Algorithm for Odor Source Localization

In this paper we tackle the problem of finding the source of a gaseous leak with a robot in a three-dimensional (3-D) physical space. The proposed method extends the operational range of the probabilistic Infotaxis algorithm [1] into 3-D and makes multiple improvements in order to increase its performance in such settings. The method has been tested systematically through high-fidelity simulations and in a wind tunnel emulating realistic conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments. The algorithm shows good performance in various environmental conditions, particularly in high wind speeds and different source release rates

Learning to predict target location with turbulent odor plumes

: Animal behavior and neural recordings show that the brain is able to measure both the intensity and the timing of odor encounters. However, whether intensity or timing of odor detections is more informative for olfactory-driven behavior is not understood. To tackle this question, we consider the problem of locating a target using the odor it releases. We ask whether the position of a target is best predicted by measures of timing vs intensity of its odor, sampled for a short period of time. To answer this question, we feed data from accurate numerical simulations of odor transport to machine learning algorithms that learn how to connect odor to target location. We find that both intensity and timing can separately predict target location even from a distance of several meters; however, their efficacy varies with the dilution of the odor in space. Thus, organisms that use olfaction from different ranges may have to switch among different modalities. This has implications on how the brain should represent odors as the target is approached. We demonstrate simple strategies to improve accuracy and robustness of the prediction by modifying odor sampling and appropriately combining distinct measures together. To test the predictions, animal behavior and odor representation should be monitored as the animal moves relative to the target, or in virtual conditions that mimic concentrated vs dilute environments

Perception and representation of temporally patterned odour stimuli in the mammalian olfactory bulb

Sensory stimuli in natural environments are dynamic and complex. The neural circuits of sensory systems in the brain are therefore adapted to extract meaningful information from this dynamic input. An attractive model system for understanding how such sensory input is processed in neural circuits is the mammalian olfactory bulb (OB). The OB has a convenient dorsal anatomical location for e.g. probe implantation, viral delivery and a well-defined circuit architecture. Furthermore, olfaction in rodent models is extremely behaviourally salient and OB circuit function can therefore be efficiently investigated in the context of behavioural response. Historically, investigation of OB function has focused on encoding of odour quality, utilising static, square pulse stimuli to explore this problem. This is in stark contrast to odour transmission in natural environments, which is governed by the chaotic structure of air turbulence, creating odour plumes. There are a number of lines of evidence suggesting that temporal information in odour plumes – the fluctuations in odour concentration within this structure – can be behaviourally relevant for olfactory based navigation and odour scene segmentation. I here posit that temporal correlations in concentration for mixtures of odours transmitted in plumes are a potential mechanism by which animals identify odour objects: mixtures of odours emanating from a common source. Using neuronal imaging, high-throughput behavioural methods, high-speed odour delivery and physical recording of odour plume dynamics, I show that temporal correlations exist between pairs of odours emanating from the same source; that mice can perceive this correlation structure and that temporal correlation is represented in the output cells of the olfactory bulb. These results indicate that mammalian olfaction operates at a higher temporal bandwidth than previously thought, and that detection of temporal features in odour signals may represent a potential mechanism for olfactory scene segmenetation

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