Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli

Predictive coding in the human olfactory system

The human olfactory system is unusual. It deviates from the classical structure and function of other sensory cortices, and many of its basic computations remain mysterious. These idiosyncrasies have challenged the development of a clear and comprehensive theoretical framework in olfactory neuroscience. To address this challenge, we develop a theory of olfactory predictive coding that aims to unify diverse olfactory phenomena. Under this scheme, the olfactory system is not merely passively processing sensory information. Instead, it is actively issuing predictions about sensory inputs before they even arrive. We map this conceptual framework onto the micro- and macroscale neurobiology of the human olfactory system and review a variety of neurobiological, computational, and behavioral evidence in support of this scheme.

Ultralow Power Optoelectronic Memtransistors Based on Vertical WS2/In2Se3 van der Waals Heterostructures

Memtransistors composed of 2D van der Waals (vdW) heterostructures are crucial for constructing artificial synaptic devices and realizing neuromorphic computing. The functional integration containing ultralow power, nonvolatile memory, and biomimetic synaptic behavior endows such devices with broad prospects. Here, we develop an optoelectronic memtransistor based on the WS2/In2Se3 vdW heterostructure and realize significant optical and electrical synaptic properties, which can simulate both short-range plasticity (STP) and long-range plasticity (LTP) of biological synapses. Under optical stimulation, the device demonstrates an ultralow power consumption (only 7.7 aJ per spike) significantly lower than biological synapses, indicating the application potential in large-scale neuromorphic hardware. Combining optical and electrical stimuli, we can perform multiple logic operations by controlling the optical and electrical inputs of the WS2/In2Se3-based memtransistor. Besides, simulated recognition utilizing the Modified National Institute of Standards and Technology data set can achieve a recognition accuracy of 85.41%. Notably, this accuracy can remain above 80% even with the introduction of Gaussian noise. These results demonstrate the promising potential of WS2/In2Se3-based memtransistors in future neuromorphic computing.

SSSort 2.0: A semi-automated spike detection and sorting system for single sensillum recordings

BACKGROUND

Single-sensillum recordings are a valuable tool for sensory research which, by their nature, access extra-cellular signals typically reflecting the combined activity of several co-housed sensory neurons. However, isolating the contribution of an individual neuron through spike-sorting has remained a major challenge due to firing rate-dependent changes in spike shape and the overlap of co-occurring spikes from several neurons. These challenges have so far made it close to impossible to investigate the responses to more complex, mixed odour stimuli.

NEW METHOD

Here we present SSSort 2.0, a method and software addressing both problems through automated and semi-automated signal processing. We have also developed a method for more objective validation of spike sorting methods based on generating surrogate ground truth data and we have tested the practical effectiveness of our software in a user study.

RESULTS

We find that SSSort 2.0 typically matches or exceeds the performance of expert manual spike sorting. We further demonstrate that, for novices, accuracy is much better with SSSort 2.0 under most conditions.

CONCLUSION

Overall, we have demonstrated that spike-sorting with SSSort 2.0 software can automate data processing of SSRs with accuracy levels comparable to, or above, expert manual performance.

Olfactory Receptor Responses to Pure Odorants in Drosophila melanogaster

Olfactory coding relies on primary information from olfactory receptor cells that respond to volatile airborne substances. Despite extensive efforts, our understanding of odor-response profiles across receptors is still poor, because of the vast number of possible ligands (odorants), the high sensitivity even to trace compounds (creating false positive responses), and the diversity of olfactory receptors. Here, we linked chemical purification with a gas chromatograph to single-receptor type recording with transgenic flies using calcium imaging to record olfactory responses to a large panel of chemicals in seven Drosophila ORs: Or10a, Or13a, Or22a, Or42b, Or47a, Or56a, and Or92a. We analyze the data using linear-nonlinear modeling and reveal that most receptors have "simple" response types (mostly positive: Or10a, Or13a, Or22a, Or47a, and Or56a). However, two receptors (Or42b and Or92a) have, in addition to "simple" responses, "complex" response types to some ligands, either positive with a negative second phase or negative with a positive second phase, suggesting the presence of multiple binding sites and/or transduction cascades. We show that some ligands reported in the literature are false positives, because of contaminations in the stimulus. We recorded all stimuli across concentrations, showing that at different concentrations, different substances appear as best ligands. Our data show that studying combinatorial olfactory coding must consider temporal response properties and odorant concentration and, in addition, is strongly influenced by the presence of trace amounts of ligands (contaminations) in the samples. These observations have important repercussions for our thinking about how animals navigate their olfactory environment.

A vector-based strategy for olfactory navigation in Drosophila

Odors serve as essential cues for navigation. Although tracking an odor plume has been modeled as a reflexive process, it remains unclear whether animals can use memories of their past odor encounters to infer the spatial structure of their chemical environment or their location within it. Here we developed a virtual-reality olfactory paradigm that allows head-fixed Drosophila to navigate structured chemical landscapes, offering insight into how memory mechanisms shape their navigational strategies. We found that flies track an appetitive odor corridor by following its boundary, alternating between rapid counterturns to exit the plume and directed returns to its edge. Using a combination of behavioral modeling, functional calcium imaging, and neural perturbations, we demonstrate that this 'edge-tracking' strategy relies on vector-based computations within the Drosophila central complex in which flies store and dynamically update memories of the direction to return them to the plume's boundary. Consistent with this, we find that FC2 neurons within the fan-shaped body, which encode a fly's navigational goal, signal the direction back to the odor boundary when flies are outside the plume. Together, our studies suggest that flies leverage the plume's boundary as a dynamic landmark to guide their navigation, analogous to the memory-based strategies other insects use for long-distance migration or homing to their nests. Plume tracking thus uses components of a conserved navigational toolkit, enabling flies to use memory mechanisms to navigate through a complex shifting chemical landscape.

Impact of developmental temperature on neural growth, connectivity, and function

Environmental temperature dictates the developmental pace of poikilothermic animals. In Drosophila, slower development at lower temperatures results in higher brain connectivity, but the generality of such scaling across temperatures and brain regions and its impact on function are unclear. Here, we show that brain connectivity scales continuously across temperatures, in agreement with a first-principle model that postulates different metabolic constraints for the growth of the brain and the organism. The model predicts brain wiring under temperature cycles and the nonuniform temporal scaling of neural development across temperatures. Developmental temperature has notable effects on odor-driven behavior. Dissecting the circuit architecture and function of neurons in the olfactory pathway, we demonstrate that developmental temperature does not alter odor encoding in first- and second-order neurons, but it shifts the specificity of connections onto third-order neurons that mediate innate behaviors. We conclude that while some circuit computations are robust to the effects of developmental temperature on wiring, others exhibit phenotypic plasticity with possible adaptive advantages.

Adaptation to visual sparsity enhances responses to isolated stimuli

Sensory systems adapt their response properties to the statistics of their inputs. For instance, visual systems adapt to low-order statistics like mean and variance to encode stimuli efficiently or to facilitate specific downstream computations. However, it remains unclear how other statistical features affect sensory adaptation. Here, we explore how Drosophila's visual motion circuits adapt to stimulus sparsity, a measure of the signal's intermittency not captured by low-order statistics alone. Early visual neurons in both ON and OFF pathways alter their responses dramatically with stimulus sparsity, responding positively to both light and dark sparse stimuli but linearly to dense stimuli. These changes extend to downstream ON and OFF direction-selective neurons, which are activated by sparse stimuli of both polarities but respond with opposite signs to light and dark regions of dense stimuli. Thus, sparse stimuli activate both ON and OFF pathways, recruiting a larger fraction of the circuit and potentially enhancing the salience of isolated stimuli. Overall, our results reveal visual response properties that increase the fraction of the circuit responding to sparse, isolated stimuli.

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Stimulus duration encoding occurs early in the moth olfactory pathway

Pheromones convey rich ethological information and guide insects' search behavior. Insects navigating in turbulent environments are tasked with the challenge of coding the temporal structure of an odor plume, obliging recognition of the onset and offset of whiffs of odor. The coding mechanisms that shape odor offset recognition remain elusive. We designed a device to deliver sharp pheromone pulses and simultaneously measured the response dynamics from pheromone-tuned olfactory receptor neurons (ORNs) in male moths and Drosophila. We show that concentration-invariant stimulus duration encoding is implemented in moth ORNs by spike frequency adaptation at two time scales. A linear-nonlinear model fully captures the underlying neural computations and offers an insight into their biophysical mechanisms. Drosophila use pheromone cis-vaccenyl acetate (cVA) only for very short distance communication and are not faced with the need to encode the statistics of the cVA plume. Their cVA-sensitive ORNs are indeed unable to encode odor-off events. Expression of moth pheromone receptors in Drosophila cVA-sensitive ORNs indicates that stimulus-offset coding is receptor independent. In moth ORNs, stimulus-offset coding breaks down for short ( < 200 ms) whiffs. This physiological constraint matches the behavioral latency of switching from the upwind surge to crosswind cast flight upon losing contact with the pheromone.

The neuron as a direct data-driven controller

In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.

Feedforward and feedback mechanisms cooperatively regulate rapid experience-dependent response adaptation in a single thermosensory neuron type

Sensory adaptation allows neurons to adjust their sensitivity and responses based on recent experience. The mechanisms that mediate continuous adaptation to stimulus history over seconds- to hours-long timescales, and whether these mechanisms can operate within a single sensory neuron type, are unclear. The single pair of AFD thermosensory neurons in Caenorhabditis elegans exhibits experience-dependent plasticity in their temperature response thresholds on both minutes- and hours-long timescales upon a temperature upshift. While long-term response adaptation requires changes in gene expression in AFD, the mechanisms driving rapid response plasticity are unknown. Here, we show that rapid thermosensory response adaptation in AFD is mediated via cGMP and calcium-dependent feedforward and feedback mechanisms operating at the level of primary thermotransduction. We find that either of two thermosensor receptor guanylyl cyclases (rGCs) alone is sufficient to drive rapid adaptation, but that each rGC drives adaptation at different rates. rGC-driven adaptation is mediated in part via phosphorylation of their intracellular domains, and calcium-dependent feedback regulation of basal cGMP levels via a neuronal calcium sensor protein. In turn, cGMP levels feedforward via cGMP-dependent protein kinases to phosphorylate a specific subunit of the cGMP-gated thermotransduction channel to further regulate rapid adaptation. Our results identify multiple molecular pathways that act in AFD to ensure rapid adaptation to a temperature change and indicate that the deployment of both transcriptional and nontranscriptional mechanisms within a single sensory neuron type can contribute to continuous sensory adaptation.

Olfactory cues and memories in animal navigation

Thierry Emonet and Massimo Vergassola discuss what research shows about how animals perform the feat of navigating by smell.

Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.

Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

Millimeter-scale animals such as Caenorhabditis elegans, Drosophila larvae, zebrafish, and bees serve as powerful model organisms in the fields of neurobiology and neuroethology. Various methods exist for recording large-scale electrophysiological signals from these animals. Existing approaches often lack, however, real-time, uninterrupted investigations due to their rigid constructs, geometric constraints, and mechanical mismatch in integration with soft organisms. The recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability, offering unique capabilities for chronic, stable interrogation and stimulation of millimeter-scale animals and miniature tissue constructs. This review summarizes the most advanced technologies for electrophysiological studies, based on methods of 3-dimensional flexible bioelectronics. A concluding section addresses the challenges of these devices in achieving freestanding, robust, and multifunctional biointerfaces.

Feedforward and feedback mechanisms cooperatively regulate rapid experience-dependent response adaptation in a single thermosensory neuron type

Sensory adaptation allows neurons to adjust their sensitivity and responses based on recent experience. The mechanisms that mediate continuous adaptation to stimulus history over seconds to hours long timescales, and whether these mechanisms can operate within a single sensory neuron type, are unclear. The single pair of AFD thermosensory neurons in C. elegans exhibits experience-dependent plasticity in their temperature response thresholds on both minutes- and hours-long timescales upon a temperature upshift. While long-term response adaptation requires changes in gene expression in AFD, the mechanisms driving rapid response plasticity are unknown. Here, we show that rapid thermosensory response adaptation in AFD is mediated via cGMP and calcium-dependent feedforward and feedback mechanisms operating at the level of primary thermotransduction. We find that either of two thermosensor receptor guanylyl cyclases (rGCs) alone is sufficient to drive rapid adaptation, but that each rGC drives adaptation at different rates. rGC-driven adaptation is mediated in part via phosphorylation of their intracellular domains, and calcium-dependent feedback regulation of basal cGMP levels via a neuronal calcium sensor protein. In turn, cGMP levels feedforward via cGMP-dependent protein kinases to phosphorylate a specific subunit of the cGMP-gated thermotransduction channel to further regulate rapid adaptation. Our results identify multiple molecular pathways that act in AFD to ensure rapid adaptation to a temperature change, and indicate that the deployment of both transcriptional and non-transcriptional mechanisms within a single sensory neuron type can contribute to continuous sensory adaptation.

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.

Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies

Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor's associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.

Extracting spatial information from temporal odor patterns: insights from insects

Extracting spatial information from temporal stimulus patterns is essential for sensory perception (e.g. visual motion direction detection or concurrent sound segregation), but this process remains understudied in olfaction. Animals rely on olfaction to locate resources and dangers. In open environments, where odors are dispersed by turbulent wind, detection of wind direction seems crucial for odor source localization. However, recent studies showed that insects can extract spatial information from the odor stimulus itself, independently from sensing wind direction. This remarkable ability is achieved by detecting the fine-scale temporal pattern of odor encounters, which contains information about the location and size of an odor source, and the distance between different odor sources.

Multifaceted luminance gain control beyond photoreceptors in Drosophila

Animals navigating in natural environments must handle vast changes in their sensory input. Visual systems, for example, handle changes in luminance at many timescales, from slow changes across the day to rapid changes during active behavior. To maintain luminance-invariant perception, visual systems must adapt their sensitivity to changing luminance at different timescales. We demonstrate that luminance gain control in photoreceptors alone is insufficient to explain luminance invariance at both fast and slow timescales and reveal the algorithms that adjust gain past photoreceptors in the fly eye. We combined imaging and behavioral experiments with computational modeling to show that downstream of photoreceptors, circuitry taking input from the single luminance-sensitive neuron type L3 implements gain control at fast and slow timescales. This computation is bidirectional in that it prevents the underestimation of contrasts in low luminance and overestimation in high luminance. An algorithmic model disentangles these multifaceted contributions and shows that the bidirectional gain control occurs at both timescales. The model implements a nonlinear interaction of luminance and contrast to achieve gain correction at fast timescales and a dark-sensitive channel to improve the detection of dim stimuli at slow timescales. Together, our work demonstrates how a single neuronal channel performs diverse computations to implement gain control at multiple timescales that are together important for navigation in natural environments.

Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odor plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency-the fraction of time signal can be detected-but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

Continuous, long-term crawling behavior characterized by a robotic transport system

Detailed descriptions of behavior provide critical insight into the structure and function of nervous systems. In Drosophila larvae and many other systems, short behavioral experiments have been successful in characterizing rapid responses to a range of stimuli at the population level. However, the lack of long-term continuous observation makes it difficult to dissect comprehensive behavioral dynamics of individual animals and how behavior (and therefore the nervous system) develops over time. To allow for long-term continuous observations in individual fly larvae, we have engineered a robotic instrument that automatically tracks and transports larvae throughout an arena. The flexibility and reliability of its design enables controlled stimulus delivery and continuous measurement over developmental time scales, yielding an unprecedented level of detailed locomotion data. We utilize the new system’s capabilities to perform continuous observation of exploratory behavior over a duration of six hours with and without a thermal gradient present, and in a single larva for over 30 hours. Long-term free-roaming behavior and analogous short-term experiments show similar dynamics that take place at the beginning of each experiment. Finally, characterization of larval thermotaxis in individuals reveals a bimodal distribution in navigation efficiency, identifying distinct phenotypes that are obfuscated when only analyzing population averages.

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.

Drosophila olfaction: past, present and future

Among the many wonders of nature, the sense of smell of the fly Drosophila melanogaster might seem, at first glance, of esoteric interest. Nevertheless, for over a century, the 'nose' of this insect has been an extraordinary system to explore questions in animal behaviour, ecology and evolution, neuroscience, physiology and molecular genetics. The insights gained are relevant for our understanding of the sensory biology of vertebrates, including humans, and other insect species, encompassing those detrimental to human health. Here, I present an overview of our current knowledge of D. melanogaster olfaction, from molecules to behaviours, with an emphasis on the historical motivations of studies and illustration of how technical innovations have enabled advances. I also highlight some of the pressing and long-term questions.

Odour motion sensing enhances navigation of complex plumes

Odour plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows^1-4. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odour signal, including odour identity⁵, intensity⁶ and timing^6-12. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including mechanosensory and visual cues^9,12,13. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odour navigation as odour-elicited upwind motion^6,8-12,14,15. Here we show that Drosophila melanogaster shape their navigational decisions using an additional directional cue-the direction of motion of odours-which they detect using temporal correlations in the odour signal between their two antennae. Using a high-resolution virtual-reality paradigm to deliver spatiotemporally complex fictive odours to freely walking flies, we demonstrate that such odour-direction sensing involves algorithms analogous to those in visual-direction sensing¹⁶. Combining simulations, theory and experiments, we show that odour motion contains valuable directional information that is absent from the airflow alone, and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odour-direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments.

A neural theory for counting memories

Keeping track of the number of times different stimuli have been experienced is a critical computation for behavior. Here, we propose a theoretical two-layer neural circuit that stores counts of stimulus occurrence frequencies. This circuit implements a data structure, called a count sketch, that is commonly used in computer science to maintain item frequencies in streaming data. Our first model implements a count sketch using Hebbian synapses and outputs stimulus-specific frequencies. Our second model uses anti-Hebbian plasticity and only tracks frequencies within four count categories ("1-2-3-many"), which trades-off the number of categories that need to be distinguished with the potential ethological value of those categories. We show how both models can robustly track stimulus occurrence frequencies, thus expanding the traditional novelty-familiarity memory axis from binary to discrete with more than two possible values. Finally, we show that an implementation of the "1-2-3-many" count sketch exists in the insect mushroom body.

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.

Mechanisms of Variability Underlying Odor-Guided Locomotion

Changes in locomotion mediated by odors (odor-guided locomotion) are an important mechanism by which animals discover resources important to their survival. Odor-guided locomotion, like most other behaviors, is highly variable. Variability in behavior can arise at many nodes along the circuit that performs sensorimotor transformation. We review these sources of variability in the context of the Drosophila olfactory system. While these sources of variability are important, using a model for locomotion, we show that another important contributor to behavioral variability is the stochastic nature of decision-making during locomotion as well as the persistence of these decisions: Flies choose the speed and curvature stochastically from a distribution and locomote with the same speed and curvature for extended periods. This stochasticity in locomotion will result in variability in behavior even if there is no noise in sensorimotor transformation. Overall, the noise in sensorimotor transformation is amplified by mechanisms of locomotion making odor-guided locomotion in flies highly variable.

Time-Dependent Odorant Sensitivity Modulation in Insects

Simple Summary

Insects, including blood-feeding female mosquitoes, can transmit deadly diseases, such as malaria, encephalitis, dengue, and yellow fever. Insects use olfaction to locate food sources, mates, and hosts. The nature of odorant plumes poses a challenge for insects in locating odorant sources in the environment. In order to modulate the system for the detection of fresh stimuli or changes in odorant concentrations, the olfaction system desensitizes to different concentrations and durations of stimuli. Without this ability, the chemotaxis behaviors of insects are defective. Thus, understanding how insects adjust their olfactory response dynamics to parse the chemical language of the external environment is not only a basic biology question but also has far-reaching implications for repellents and pest control.

Abstract

Insects use olfaction to detect ecologically relevant chemicals in their environment. To maintain useful responses over a variety of stimuli, olfactory receptor neurons are desensitized to prolonged or high concentrations of stimuli. Depending on the timescale, the desensitization is classified as short-term, which typically spans a few seconds; or long-term, which spans from minutes to hours. Compared with the well-studied mechanisms of desensitization in vertebrate olfactory neurons, the mechanisms underlying invertebrate olfactory sensitivity regulation remain poorly understood. Recently, using a large-scale functional screen, a conserved critical receptor phosphorylation site has been identified in the model insect Drosophila melanogaster, providing new insight into the molecular basis of desensitization in insects. Here, we summarize the progress in this area and provide perspectives on future directions to determine the molecular mechanisms that orchestrate the desensitization in insect olfaction.

Predicting and Manipulating Cone Responses to Naturalistic Inputs

Primates explore their visual environment by making frequent saccades, discrete and ballistic eye movements that direct the fovea to specific regions of interest. Saccades produce large and rapid changes in input. The magnitude of these changes and the limited signaling range of visual neurons mean that effective encoding requires rapid adaptation. Here, we explore how macaque cone photoreceptors maintain sensitivity under these conditions. Adaptation makes cone responses to naturalistic stimuli highly nonlinear and dependent on stimulus history. Such responses cannot be explained by linear or linear-nonlinear models but are well explained by a biophysical model of phototransduction based on well-established biochemical interactions. The resulting model can predict cone responses to a broad range of stimuli and enables the design of stimuli that elicit specific (e.g., linear) cone photocurrents. These advances will provide a foundation for investigating the contributions of cone phototransduction and post-transduction processing to visual function.SIGNIFICANCE STATEMENT We know a great deal about adaptational mechanisms that adjust sensitivity to slow changes in visual inputs such as the rising or setting sun. We know much less about the rapid adaptational mechanisms that are essential for maintaining sensitivity as gaze shifts around a single visual scene. We characterize how phototransduction in cone photoreceptors adapts to rapid changes in input similar to those encountered during natural vision. We incorporate these measurements into a quantitative model that can predict cone responses across a broad range of stimuli. This model not only shows how cone phototransduction aids the encoding of natural inputs but also provides a tool to identify the role of the cone responses in shaping those of downstream visual neurons.

Sensing complementary temporal features of odor signals enhances navigation of diverse turbulent plumes

We and others have shown that during odor plume navigation, walking Drosophila melanogaster bias their motion upwind in response to both the frequency of their encounters with the odor (Demir et al., 2020) and the intermittency of the odor signal, which we define to be the fraction of time the signal is above a detection threshold (Alvarez-Salvado et al., 2018). Here, we combine and simplify previous mathematical models that recapitulated these data to investigate the benefits of sensing both of these temporal features and how these benefits depend on the spatiotemporal statistics of the odor plume. Through agent-based simulations, we find that navigators that only use frequency or intermittency perform well in some environments - achieving maximal performance when gains are near those inferred from experiment - but fail in others. Robust performance across diverse environments requires both temporal modalities. However, we also find a steep trade-off when using both sensors simultaneously, suggesting a strong benefit to modulating how much each sensor is weighted, rather than using both in a fixed combination across plumes. Finally, we show that the circuitry of the Drosophila olfactory periphery naturally enables simultaneous intermittency and frequency sensing, enhancing robust navigation through a diversity of odor environments. Together, our results suggest that the first stage of olfactory processing selects and encodes temporal features of odor signals critical to real-world navigation tasks.

How the insect central complex could coordinate multimodal navigation

The central complex of the insect midbrain is thought to coordinate insect guidance strategies. Computational models can account for specific behaviours, but their applicability across sensory and task domains remains untested. Here, we assess the capacity of our previous model (Sun et al. 2020) of visual navigation to generalise to olfactory navigation and its coordination with other guidance in flies and ants. We show that fundamental to this capacity is the use of a biologically plausible neural copy-and-shift mechanism that ensures sensory information is presented in a format compatible with the insect steering circuit regardless of its source. Moreover, the same mechanism is shown to allow the transfer cues from unstable/egocentric to stable/geocentric frames of reference, providing a first account of the mechanism by which foraging insects robustly recover from environmental disturbances. We propose that these circuits can be flexibly repurposed by different insect navigators to address their unique ecological needs.

Motion vision: Pinning down motion computation in an ever-changing circuit

A new electrophysiological study of the Drosophila visual system, recording from columnar inputs to motion-detecting neurons, has provided new insights into the computations that underlie motion vision.

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic ("ephaptic") interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. We find that NSIs improve mixture ratio detection and plume structure sensing and do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. The best performance is achieved when both mechanisms work in synergy. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.

The neural basis for violations of Weber's law in self-motion perception

A prevailing view is that Weber's law constitutes a fundamental principle of perception. This widely accepted psychophysical law states that the minimal change in a given stimulus that can be perceived increases proportionally with amplitude and has been observed across systems and species in hundreds of studies. Importantly, however, Weber's law is actually an oversimplification. Notably, there exist violations of Weber's law that have been consistently observed across sensory modalities. Specifically, perceptual performance is better than that predicted from Weber's law for the higher stimulus amplitudes commonly found in natural sensory stimuli. To date, the neural mechanisms mediating such violations of Weber's law in the form of improved perceptual performance remain unknown. Here, we recorded from vestibular thalamocortical neurons in rhesus monkeys during self-motion stimulation. Strikingly, we found that neural discrimination thresholds initially increased but saturated for higher stimulus amplitudes, thereby causing the improved neural discrimination performance required to explain perception. Theory predicts that stimulus-dependent neural variability and/or response nonlinearities will determine discrimination threshold values. Using computational methods, we thus investigated the mechanisms mediating this improved performance. We found that the structure of neural variability, which initially increased but saturated for higher amplitudes, caused improved discrimination performance rather than response nonlinearities. Taken together, our results reveal the neural basis for violations of Weber's law and further provide insight as to how variability contributes to the adaptive encoding of natural stimuli with continually varying statistics.

Using your nose to find your way: Ethological comparisons between human and non-human species

Olfaction is arguably the least valued among our sensory systems, and its significance for human behavior is often neglected. Spatial navigation represents no exception to the rule: humans are often characterized as purely visual navigators, a view that undermines the contribution of olfactory cues. Accordingly, research investigating whether and how humans use olfaction to navigate space is rare. In comparison, research on olfactory navigation in non-human species is abundant, and identifies behavioral strategies along with neural mechanisms characterizing the use of olfactory cues during spatial tasks. Using an ethological approach, our review draws from studies on olfactory navigation across species to describe the adaptation of strategies under the influence of selective pressure. Mammals interact with spatial environments by abstracting multisensory information into cognitive maps. We thus argue that olfactory cues, alongside inputs from other sensory modalities, play a crucial role in spatial navigation for mammalian species, including humans; that is, odors constitute one of the many building blocks in the formation of cognitive maps.

Efficient and adaptive sensory codes

The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.

PKC98E Regulates Odorant Responses in Drosophila melanogaster

Drosophila odorant receptors (Ors) are ligand gated ion channels composed of a common receptor subunit Or co-receptor (ORCO) and one of 62 "tuning" receptor subunits that confer odorant specificity to olfactory neuron responses. Like other sensory systems studied to date, exposing Drosophila olfactory neurons to activating ligands results in reduced responses to subsequent exposures through a process called desensitization. We recently showed that phosphorylation of serine 289 on the common Or subunit ORCO is required for normal peak olfactory neuron responses. Dephosphorylation of this residue occurs on prolonged odorant exposure, and underlies the slow modulation of olfactory neuron responses we term "slow desensitization." Slow desensitization results in the reduction of peak olfactory neuron responses and flattening of dose-response curves, implicating changes in ORCO^S289 phosphorylation state as an important modulator of olfactory neuron responses. Here, we report the identification of the primary kinase responsible for ORCO^S289 phosphorylation, PKC98E. Antiserum localizes the kinase to the dendrites of the olfactory neurons. Deletion of the kinase from olfactory neurons in the naive state (the absence of prolonged odor exposure) reduces ORCO^S289 phosphorylation and reduces peak odorant responses without altering receptor localization or expression levels. Genetic rescue with a PKC98E predicted to be constitutively active restores ORCO S289 phosphorylation and olfactory neuron sensitivity to the PKC98E mutants in the naive state. However, the dominant kinase is defective for slow desensitization. Together, these findings reveal that PKC98E is an important regulator of ORCO receptors and olfactory neuron function.SIGNIFICANCE STATEMENT We have identified PKC98E as the kinase responsible for phosphorylation of the odorant receptor co-receptor (ORCO) at S289 that is required for normal odorant response kinetics of olfactory neurons. This is a significant step toward revealing the enzymology underlying the regulation of odorant response regulation in insects.

Effects of Multi-Component Backgrounds of Volatile Plant Compounds on Moth Pheromone Perception

Simple Summary

It is well acknowledged that some of the volatile plant compounds (VPC) naturally present in insect natural habitats alter the perception of their own pheromone when presented individually as a background to pheromone. However, the effects of mixing VPCs as they appear to insects in natural olfactory landscapes are poorly understood. We measured the activity of brain neurons and neurons that detect a sex pheromone component in a moth antenna, while exposed to simple or composite backgrounds of VPCs representative of the odorant variety encountered by this moth. Maps of activities were built using calcium imaging to visualize which brain areas were most affected by VPCs. In the antenna, we observed differences in VPC capacity to elicit firing response that cannot be explained by differences in stimulus intensities because we adjusted concentrations according to volatility. The neuronal network, which reformats the input from antenna neurons in the brain, did not improve pheromone salience. We postulate that moth olfactory system evolved to increase sensitivity and encode fast changes of concentration at some cost for signal extraction. Comparing blends to single compounds indicated that a blend shows the activity of its most active component, VPC salience seems more important than background complexity.

Abstract

The volatile plant compounds (VPC) alter pheromone perception by insects but mixture effects inside insect olfactory landscapes are poorly understood. We measured the activity of receptor neurons tuned to Z7-12Ac (Z7-ORN), a pheromone component, in the antenna and central neurons in male Agrotis ipsilon while exposed to simple or composite backgrounds of a panel of VPCs representative of the odorant variety encountered by a moth. Maps of activities were built using calcium imaging to visualize which areas in antennal lobes (AL) were affected by VPCs. We compared the VPC activity and their impact as backgrounds at antenna and AL levels, individually or in blends. At periphery, VPCs showed differences in their capacity to elicit Z7-ORN firing response that cannot be explained by differences in stimulus intensities because we adjusted concentrations according to vapor pressures. The AL neuronal network, which reformats the ORN input, did not improve pheromone salience. We postulate that the AL network evolved to increase sensitivity and to encode for fast changes of pheromone at some cost for signal extraction. Comparing blends to single compounds indicated that a blend shows the activity of its most active component. VPC salience seems to be more important than background complexity.

Olfactory navigation in the real world: Simple local search strategies for turbulent environments

Olfaction informs animal navigation for foraging, social interaction, and threat evasion. However, turbulent flow on the spatial scales of most animal navigation leads to intermittent odor information and presents a challenge to simple gradient-ascent navigation. Here we present two strategies for iterative gradient estimation and navigation via olfactory cues in 2D space: tropotaxis, spatial concentration comparison (i.e., instantaneous comparison between lateral olfactory sensors on a navigating animal) and klinotaxis, spatiotemporal concentration comparison (i.e., comparison between two subsequent concentration samples as the animal moves through space). We then construct a hybrid model that uses klinotaxis but utilizes tropotactic information to guide its spatial sampling strategy. We find that for certain body geometries in which bilateral sensors are closely-spaced (e.g., mammalian nares), klinotaxis outperforms tropotaxis; for widely-spaced sensors (e.g., arthropod antennae), tropotaxis outperforms klinotaxis. We find that both navigation strategies perform well on smooth odor gradients and are robust against noisy gradients represented by stochastic odor models and real turbulent flow data. In some parameter regimes, the hybrid model outperforms klinotaxis alone, but not tropotaxis.

Adaptive temporal processing of odor stimuli

The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.

Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters

How insects navigate complex odor plumes, where the location and timing of odor packets are uncertain, remains unclear. Here we imaged complex odor plumes simultaneously with freely-walking flies, quantifying how behavior is shaped by encounters with individual odor packets. We found that navigation was stochastic and did not rely on the continuous modulation of speed or orientation. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Further, flies used the timing of odor encounters to modulate the transition rates between walks and stops. In more regular environments, flies continuously modulate speed and orientation, even though encounters can still occur randomly due to animal motion. We find that in less predictable environments, where encounters are random in both space and time, walking flies navigate with random walks biased by encounter timing.

Adaptive tuning of cell sensory diversity without changes in gene expression

In the face of uncertainty, cell populations tend to diversify to enhance survival and growth. Previous studies established that cells can optimize such bet hedging upon environmental change by modulating gene expression to adapt both the average and diversity of phenotypes. Here, we demonstrate that cells can tune phenotypic diversity also using posttranslational modifications. In the chemotaxis network of Escherichia coli, we find, for both major chemoreceptors Tar and Tsr, that cell-to-cell variation in response sensitivity is dynamically modulated depending on the presence or absence of their cognate chemoeffector ligands in the environment. Combining experiments with mathematical modeling, we show that this diversity tuning requires only the environment-dependent covalent modification of chemoreceptors and a standing cell-to-cell variation in their allosteric coupling. Thus, when environmental cues are unavailable, phenotypic diversity enhances the population's readiness for many signals. However, once a signal is perceived, the population focuses on tracking that signal.

Molecular mechanisms of olfactory detection in insects: beyond receptors

Insects thrive in diverse ecological niches in large part because of their highly sophisticated olfactory systems. Over the last two decades, a major focus in the study of insect olfaction has been on the role of olfactory receptors in mediating neuronal responses to environmental chemicals. In vivo, these receptors operate in specialized structures, called sensilla, which comprise neurons and non-neuronal support cells, extracellular lymph fluid and a precisely shaped cuticle. While sensilla are inherent to odour sensing in insects, we are only just beginning to understand their construction and function. Here, we review recent work that illuminates how odour-evoked neuronal activity is impacted by sensillar morphology, lymph fluid biochemistry, accessory signalling molecules in neurons and the physiological crosstalk between sensillar cells. These advances reveal multi-layered molecular and cellular mechanisms that determine the selectivity, sensitivity and dynamic modulation of odour-evoked responses in insects.

Mini-brain computations converting dynamic olfactory inputs into orientation behavior

The neural logic underlying the conversion of non-stationary (dynamic) olfactory inputs into odor-search behaviors has been difficult to crack due to the distributed nature of the olfactory code - food odors typically co-activate multiple olfactory sensory neurons. In the Drosophila larva, the activity of a single olfactory sensory neuron is sufficient to direct accurate reorientation maneuvers in odor gradients (chemotaxis). In this reduced sensory system, a descending pathway essential for larval chemotaxis has been delineated from the peripheral olfactory system down to the premotor system. Here, I review how anatomical and functional inspections of this pathway have advanced our understanding of the neural mechanisms that convert behaviorally relevant sensory signals into orientation responses.

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies.. [DOI: 10.1101/2020.07.23.217216]

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla.

We found that NSIs improve mixture ratio detection and plume structure sensing and they do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.

Author summary

Myelin is important to isolate neurons and avoid disruptive electrical interference between them; it can be found in almost any neural assembly. However, there are a few exceptions to this rule and it remains unclear why. One particularly interesting case is the electrical interaction between olfactory sensory neurons co-housed in the sensilla of insects. Here, we created a computational model of the early stages of the Drosophila olfactory system and observed that the electrical interference between olfactory receptor neurons can be a useful trait that can help flies, and other insects, to navigate the complex plumes of odorants in their natural environment.

With the model we were able to shed new light on the trade-off of adopting this mechanism: We found that the non-synaptic interactions (NSIs) improve both the identification of the concentration ratio in mixtures of odorants and the discrimination of odorant mixtures emanating from a single source from odorants emitted from separate sources – both highly advantageous. However, they also decrease the dynamic range of the olfactory sensory neurons – a clear disadvantage.

Habituation as a neural algorithm for online odor discrimination

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.

A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna

Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Identity and concentration, intrinsically embedded in the odorant space, are two key characteristics of olfactory coding that define the level of complexity of neural processing throughout the olfactory system in the fruit fly. In this paper we advance a theoretical foundation for understanding these two characteristics by quantifying mathematically the odorant space and devising a biophysical model of the olfactory sensory neurons (OSNs). To validate our modeling approach, we propose and apply an algorithm to estimate the affinity value and the dissociation rate, the two characteristics that define odorant identity, of multiple odorant-receptor pairs. We then evaluate our model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. Furthermore, we evaluate the model at the OSN population level and quantify on the molecular level the spatio-temporal level of complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent spatio-temporal code determines the level of complexity of the input space driving olfactory processing in the downstream neuropils. Lastly, our model demonstrates that the currently available data for OSN responses only enables estimation of affinity value. This calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.