Faculty Bibliography

Spatial navigation is influenced by cues from nearly every sensory modality and thus provides an excellent model for understanding how different sensory streams are integrated to drive behavior. Here we review recent work on multisensory control of navigation in the model organism Drosophila melanogaster, which allows for detailed circuit dissection. We identify four modes of integration that have been described in the literature-suppression, gating, summation, and association-and describe regions of the larval and adult brain that have been implicated in sensory integration. Finally we discuss what circuit architectures might support these different forms of integration. We argue that Drosophila is an excellent model to discover these circuit and biophysical motifs.

Wind is a major navigational cue for insects, but how wind direction is decoded by central neurons in the insect brain is unknown. Here we find that walking flies combine signals from both antennae to orient to wind during olfactory search behavior. Movements of single antennae are ambiguous with respect to wind direction, but the difference between left and right antennal displacements yields a linear code for wind direction in azimuth. Second-order mechanosensory neurons share the ambiguous responses of a single antenna and receive input primarily from the ipsilateral antenna. Finally, we identify novel "wedge projection neurons" that integrate signals across the two antennae and receive input from at least three classes of second-order neurons to produce a more linear representation of wind direction. This study establishes how a feature of the sensory environment-wind direction-is decoded by neurons that compare information across two sensors.

The demands on a sensory system depend not only on the statistics of its inputs but also on the task. In olfactory navigation, for example, the task is to find the plume source; allocation of sensory resources may therefore be driven by aspects of the plume that are informative about source location, rather than concentration per se. Here we explore the implications of this idea for encoding odor concentration.To formalize the notion that sensory resources are limited, we considered coding strategies that partitioned the odor concentration range into a set of discriminable intervals. We developed a dynamic programming algorithm that, given the distribution of odor concentrations at several locations, determines the partitioning that conveys the most information about location. We applied this analysis to planar laser-induced fluorescence measurements of spatiotemporal odor fields with realistic advection speeds (5 to 20 cm/sec), with or without a nearby boundary or obstacle. Across all environments, the optimal coding strategy allocated more resources (i.e., more and finer discriminable intervals) to the upper end of the concentration range than would be expected from histogram equalization, the optimal strategy if the goal were to reconstruct the plume, rather than to navigate. Finally, we show that ligand binding, as captured by the Hill equation, transforms odorant concentration into response levels in a way that approximates information maximization for navigation. This behavior occurs when the Hill dissociation constant is near the mean odor concentration, an adaptive set-point that has been observed in the olfactory system of flies.SIGNIFICANCE STATEMENTThe first step of olfactory processing is receptor binding, and the resulting relationship between odorant concentration and the bound receptor fraction is a saturating one. While this Hill nonlinearity can be viewed as a distortion that is imposed by the biophysics of receptor binding, here we show that it also plays an important information-processing role in olfactory navigation. Specifically, by combining a novel dynamic-programming algorithm with physical measurements of turbulent plumes, we determine the optimal strategy for encoding odor concentration when the goal is to determine location. This strategy is distinct from histogram equalization, the strategy that maximizes information about plume concentration, and is closely approximated by the Hill nonlinearity when the binding constant is near the ambient mean.

A longstanding goal of systems neuroscience is to quantitatively describe how the brain integrates sensory cues over time. Here, we develop a closed-loop orienting paradigm in Drosophila to study the algorithms by which cues from two modalities are integrated during ongoing behavior. We find that flies exhibit two behaviors when presented simultaneously with an attractive visual stripe and aversive wind cue. First, flies perform a turn sequence where they initially turn away from the wind but later turn back toward the stripe, suggesting dynamic sensory processing. Second, turns toward the stripe are slowed by the presence of competing wind, suggesting summation of turning drives. We develop a model in which signals from each modality are filtered in space and time to generate turn commands and then summed to produce ongoing orienting behavior. This computational framework correctly predicts behavioral dynamics for a range of stimulus intensities and spatial arrangements.

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.

Odor attraction in walking Drosophila melanogaster is commonly used to relate neural function to behavior, but the algorithms underlying attraction are unclear. Here, we develop a high-throughput assay to measure olfactory behavior in response to well-controlled sensory stimuli. We show that odor evokes two behaviors: an upwind run during odor (ON response), and a local search at odor offset (OFF response). Wind orientation requires antennal mechanoreceptors, but search is driven solely by odor. Using dynamic odor stimuli, we measure the dependence of these two behaviors on odor intensity and history. Based on these data, we develop a navigation model that recapitulates the behavior of flies in our apparatus, and generates realistic trajectories when run in a turbulent boundary layer plume. The ability to parse olfactory navigation into quantifiable elementary sensori-motor transformations provides a foundation for dissecting neural circuits that govern olfactory behavior.

Many species rely on olfaction to navigate towards food sources or mates. Olfactory navigation is a challenging task since odor environments are typically turbulent. While time-averaged odor concentration varies smoothly with the distance to the source, instaneous concentrations are intermittent and obtaining stable averages takes longer than the typical intervals between animals' navigation decisions. How to effectively sample from the odor distribution to determine sampling location is the focus on this article. To investigate which sampling strategies are most informative about the location of an odor source, we recorded three naturalistic stimuli with planar lased-induced fluorescence and used an information-theoretic approach to quantify the information that different sampling strategies provide about sampling location. Specifically, we compared multiple sampling strategies based on a fixed number of coding bits for encoding the olfactory stimulus. When the coding bits were all allocated to representing odor concentration at a single sensor, information rapidly saturated. Using the same number of coding bits in two sensors provides more information, as does coding multiple samples at different times. When accumulating multiple samples at a fixed location, the temporal sequence does not yield a large amount of information and can be averaged with minimal loss. Furthermore, we show that histogram-equalization is not the most efficient way to use coding bits when using the olfactory sample to determine location.

High-throughput electron microscopy has started to reveal synaptic connectivity maps of single circuits and whole brain regions, for example, in the Drosophila olfactory system. However, efficacy, timing, and frequency tuning of synaptic vesicle release are also highly diversified across brain synapses. These features critically depend on the nanometer-scale coupling distance between voltage-gated Ca²⁺ channels (VGCCs) and the synaptic vesicle release machinery. Combining light super resolution microscopy with in vivo electrophysiology, we show here that two orthogonal scaffold proteins (ELKS family Bruchpilot, BRP, and Syd-1) cluster-specific (M)Unc13 release factor isoforms either close (BRP/Unc13A) or further away (Syd-1/Unc13B) from VGCCs across synapses of the Drosophila olfactory system, resulting in different synapse-characteristic forms of short-term plasticity. Moreover, BRP/Unc13A versus Syd-1/Unc13B ratios were different between synapse types. Thus, variation in tightly versus loosely coupled scaffold protein/(M)Unc13 modules can tune synapse-type-specific release features, and "nanoscopic molecular fingerprints" might identify synapses with specific temporal features.

Local inhibitory neurons control the timing of neural activity in many circuits. To understand how inhibition controls timing, it is important to understand the dynamics of activity in populations of local inhibitory interneurons, as well as the mechanisms that underlie these dynamics. Here we describe thein vivoresponse dynamics of a large population of inhibitory local neurons (LNs) in theDrosophila melanogasterantennal lobe, the analog of the vertebrate olfactory bulb, and we dissect the network and intrinsic mechanisms that give rise to these dynamics. Some LNs respond to odor onsets ("ON" cells) and others to offsets ("OFF" cells), whereas still others respond at both times. Moreover, different LNs signal odor concentration fluctuations on different timescales. Some respond rapidly, and can track rapid concentration fluctuations. Others respond slowly, and are best at tracking slow fluctuations. We found a continuous spectrum of preferred stimulation timescales among LNs, as well as a continuum of ON-OFF behavior. Usingin vivowhole-cell recordings, we show that the timing of an LN's response (ON vs OFF) can be predicted from the interplay of excitatory and inhibitory synaptic currents that it receives. Meanwhile, the preferred timescale of an LN is related to its intrinsic properties. These results illustrate how a population of inhibitory interneurons can collectively encode bidirectional changes in stimulus intensity on multiple timescales, and how this can arise via an interaction between synaptic and intrinsic mechanisms. SIGNIFICANCE STATEMENT: Most neural circuits contain diverse populations of inhibitory interneurons. The way inhibition shapes network activity will depend on the spiking dynamics of the interneuron population. Here we describe the dynamics of activity in a large population of inhibitory interneurons in the first brain relay of the fruit fly olfactory system. Because odor plumes fluctuate on multiple timescales, the drive to this circuit can vary over a range of frequencies. We show how synaptic and cellular mechanisms interact to recruit different interneurons at different times, and in response to different temporal features of odor stimuli. As a result, inhibition is recruited over a range of conditions, and there is the potential to tune the timing of inhibition as the environment changes.