Projection neurons in Drosophila antennal lobes signal the acceleration of odor concentrations

Modeling and characterization of pure and odorant mixture processing in the Drosophila mushroom body calyx

Associative memory in the Mushroom Body of the fruit fly brain depends on the encoding and processing of odorants in the first three stages of the Early Olfactory System: the Antenna, the Antennal Lobe and the Mushroom Body Calyx. The Kenyon Cells (KCs) of the Calyx provide the Mushroom Body compartments the identity of pure and odorant mixtures encoded as a train of spikes. Characterizing the code underlying the KC spike trains is a major challenge in neuroscience. To address this challenge we start by explicitly modeling the space of odorants using constructs of both semantic and syntactic information. Odorant semantics concerns the identity of odorants while odorant syntactics pertains to their concentration amplitude. These odorant attributes are multiplicatively coupled in the process of olfactory transduction. A key question that early olfactory systems must address is how to disentangle the odorant semantic information from the odorant syntactic information. To address the untanglement we devised an Odorant Encoding Machine (OEM) modeling the first three stages of early olfactory processing in the fruit fly brain. Each processing stage is modeled by Divisive Normalization Processors (DNPs). DNPs are spatio-temporal models of canonical computation of brain circuits. The end-to-end OEM is constructed as cascaded DNPs. By extensively modeling and characterizing the processing of pure and odorant mixtures in the Calyx, we seek to answer the question of its functional significance. We demonstrate that the DNP circuits in the OEM combinedly reduce the variability of the Calyx response to odorant concentration, thereby separating odorant semantic information from syntactic information. We then advance a code, called first spike sequence code, that the KCs make available at the output of the Calyx. We show that the semantics of odorants can be represented by this code in the spike domain and is ready for easy memory access in the Mushroom Body compartments.

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.

Response Plasticity of Drosophila Olfactory Sensory Neurons

In insect olfaction, sensitization refers to the amplification of a weak olfactory signal when the stimulus is repeated within a specific time window. In the vinegar fly, Drosophila melanogaster, this occurs already at the periphery, at the level of olfactory sensory neurons (OSNs) located in the antenna. In our study, we investigate whether sensitization is a widespread property in a set of seven types of OSNs, as well as the mechanisms involved. First, we characterize and compare the differences in spontaneous activity, response velocity and response dynamics, among the selected OSN types. These express different receptors with distinct tuning properties and behavioral relevance. Second, we show that sensitization is not a general property. Among our selected OSN types, it occurs in those responding to more general food odors, while OSNs involved in very specific detection of highly specific ecological cues like pheromones and warning signals show no sensitization. Moreover, we show that mitochondria play an active role in sensitization by contributing to the increase in intracellular Ca2+ upon weak receptor activation. Thus, by using a combination of single sensillum recordings (SSRs), calcium imaging and pharmacology, we widen the understanding of how the olfactory signal is processed at the periphery.

State and rate-of-change encoding in parallel mesoaccumbal dopamine pathways

The nervous system uses fast- and slow-adapting sensory detectors in parallel to enable neuronal representations of external states and their temporal dynamics. It is unknown whether this dichotomy also applies to internal representations that have no direct correlation in the physical world. Here we find that two distinct dopamine (DA) neuron subtypes encode either a state or its rate-of-change. In mice performing a reward-seeking task, we found that the animal's behavioral state and rate-of-change were encoded by the sustained activity of DA neurons in medial ventral tegmental area (VTA) DA neurons and transient activity in lateral VTA DA neurons, respectively. The neural activity patterns of VTA DA cell bodies matched DA release patterns within anatomically defined mesoaccumbal pathways. Based on these results, we propose a model in which the DA system uses two parallel lines for proportional-differential encoding of a state variable and its temporal dynamics.

Rapid threat assessment in the Drosophila thermosensory system

Neurons that participate in sensory processing often display "ON" responses, i.e., fire transiently at the onset of a stimulus. ON transients are widespread, perhaps universal to sensory coding, yet their function is not always well-understood. Here, we show that ON responses in the Drosophila thermosensory system extrapolate the trajectory of temperature change, priming escape behavior if unsafe thermal conditions are imminent. First, we show that second-order thermosensory projection neurons (TPN-IIIs) and their Lateral Horn targets (TLHONs), display ON responses to thermal stimuli, independent of direction of change (heating or cooling) and of absolute temperature. Instead, they track the rate of temperature change, with TLHONs firing exclusively to rapid changes (>0.2 °C/s). Next, we use connectomics to track TLHONs' output to descending neurons that control walking and escape, and modeling and genetic silencing to demonstrate how ON transients can flexibly amplify aversive responses to small thermal change. Our results suggest that, across sensory systems, ON transients may represent a general mechanism to systematically anticipate and respond to salient or dangerous conditions.

Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.

The functional logic of odor information processing in the Drosophila antennal lobe

Recent advances in molecular transduction of odorants in the Olfactory Sensory Neurons (OSNs) of the Drosophila Antenna have shown that the odorant object identity is multiplicatively coupled with the odorant concentration waveform. The resulting combinatorial neural code is a confounding representation of odorant semantic information (identity) and syntactic information (concentration). To distill the functional logic of odor information processing in the Antennal Lobe (AL) a number of challenges need to be addressed including 1) how is the odorant semantic information decoupled from the syntactic information at the level of the AL, 2) how are these two information streams processed by the diverse AL Local Neurons (LNs) and 3) what is the end-to-end functional logic of the AL? By analyzing single-channel physiology recordings at the output of the AL, we found that the Projection Neuron responses can be decomposed into a concentration-invariant component, and two transient components boosting the positive/negative concentration contrast that indicate onset/offset timing information of the odorant object. We hypothesized that the concentration-invariant component, in the multi-channel context, is the recovered odorant identity vector presented between onset/offset timing events. We developed a model of LN pathways in the Antennal Lobe termed the differential Divisive Normalization Processors (DNPs), which robustly extract the semantics (the identity of the odorant object) and the ON/OFF semantic timing events indicating the presence/absence of an odorant object. For real-time processing with spiking PN models, we showed that the phase-space of the biological spike generator of the PN offers an intuit perspective for the representation of recovered odorant semantics and examined the dynamics induced by the odorant semantic timing events. Finally, we provided theoretical and computational evidence for the functional logic of the AL as a robust ON-OFF odorant object identity recovery processor across odorant identities, concentration amplitudes and waveform profiles.

Spike frequency adaptation facilitates the encoding of input gradient in insect olfactory projection neurons

The olfactory system in insects has evolved to process the dynamic changes in the concentration of food odors or sex pheromones to localize the nutrients or conspecific mating partners. Experimental studies have suggested that projection neurons (PNs) in insects encode not only the stimulus intensity but also its rate-of-change (input gradient). In this study, we aim to develop a simple computational model for a PN to understand the mechanism underlying the coding of the rate-of-change information. We show that the spike frequency adaptation is a potential key mechanism for reproducing the phasic response pattern of the PN in Drosophila. We also demonstrate that this adaptation mechanism enables the PN to encode the rate-of-change of the input firing rate. Finally, our model predicts that the PN exhibits the intensity-invariant response for the pulse and ramp odor stimulus. These results suggest that the developed model is useful for investigating the coding principle underlying olfactory information processing in insects.

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.

Short-Term Plasticity Regulates Both Divisive Normalization and Adaptive Responses in Drosophila Olfactory System

In Drosophila, olfactory information received by olfactory receptor neurons (ORNs) is first processed by an incoherent feed forward neural circuit in the antennal lobe (AL) that consists of ORNs (input), inhibitory local neurons (LNs), and projection neurons (PNs). This “early” olfactory information processing has two important characteristics. First, response of a PN to its cognate ORN is normalized by the overall activity of other ORNs, a phenomenon termed “divisive normalization.” Second, PNs respond strongly to the onset of ORN activities, but they adapt to prolonged or continuously varying inputs. Despite the importance of these characteristics for learning and memory, their underlying mechanisms are not fully understood. Here, we develop a circuit model for describing the ORN-LN-PN dynamics by including key neuron-neuron interactions such as short-term plasticity (STP) and presynaptic inhibition (PI). By fitting our model to experimental data quantitatively, we show that a strong STP balanced between short-term facilitation (STF) and short-term depression (STD) is responsible for the observed nonlinear divisive normalization in Drosophila. Our circuit model suggests that either STP or PI alone can lead to adaptive response. However, by comparing our model results with experimental data, we find that both STP and PI work together to achieve a strong and robust adaptive response. Our model not only helps reveal the mechanisms underlying two main characteristics of the early olfactory process, it can also be used to predict PN responses to arbitrary time-dependent signals and to infer microscopic properties of the circuit (such as the strengths of STF and STD) from the measured input-output relation. Our circuit model may be useful for understanding the role of STP in other sensory systems.

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.

Synchronous and opponent thermosensors use flexible cross-inhibition to orchestrate thermal homeostasis

Body temperature homeostasis is essential and reliant upon the integration of outputs from multiple classes of cooling- and warming-responsive cells. The computations that integrate these outputs are not understood. Here, we discover a set of warming cells (WCs) and show that the outputs of these WCs combine with previously described cooling cells (CCs) in a cross-inhibition computation to drive thermal homeostasis in larval Drosophila WCs and CCs detect temperature changes using overlapping combinations of ionotropic receptors: Ir68a, Ir93a, and Ir25a for WCs and Ir21a, Ir93a, and Ir25a for CCs. WCs mediate avoidance to warming while cross-inhibiting avoidance to cooling, and CCs mediate avoidance to cooling while cross-inhibiting avoidance to warming. Ambient temperature-dependent regulation of the strength of WC- and CC-mediated cross-inhibition keeps larvae near their homeostatic set point. Using neurophysiology, quantitative behavioral analysis, and connectomics, we demonstrate how flexible integration between warming and cooling pathways can orchestrate homeostatic thermoregulation.

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.

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.

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.

Developing and testing of an air dilution flow olfactometer with known rates of concentration change

BACKGROUND

Concentration is a variable aspect of an odor signal and determines the operation range of olfactory receptor neurons (ORNs). A concentration increase is perceived as an odor stimulus. The role that the rate of concentration increase plays thereby has been studied with electrophysiological techniques in ORNs of the cockroach. A key prerequisite for these studies was the development of an air dilution flow olfactometer that allowed testing the same change in concentration at various rates.

NEW METHOD

The rate of concentration change was controlled and varied by changing the mixing ratio of odor-saturated and clean air by means of proportional valves. Their input voltages were phase shifted by 180° to hold the mixed air at a particular constant volume flow rate.

RESULTS

Using this stimulation technique, we identified, in a morphologically distinct sensillum on the cockroach's antenna, antagonistically responding ON and OFF ORNs which display a high sensitivity for slow changes in odor concentration.

COMPARISON WITH EXISTING METHODS

The olfactometer is unique because it enables delivering slowly oscillating concentration changes. By varying the oscillation period, the individual effects of the instantaneous odor concentration and its rate of change on the ORNs' responses can be determined.

CONCLUSIONS

The olfactometer provides a new experimental approach in the study of odor coding and opens the door for improved comparative studies on olfactory systems. It would be important to gain insight into the ORNs' ability to detect the rate of concentration change in other insects that use odors for orientation in different contexts.

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.

Controlling and measuring dynamic odorant stimuli in the laboratory

Animals experience complex odorant stimuli that vary widely in composition, intensity and temporal properties. However, stimuli used to study olfaction in the laboratory are much simpler. This mismatch arises from the challenges in measuring and controlling them precisely and accurately. Even simple pulses can have diverse kinetics that depend on their molecular identity. Here, we introduce a model that describes how stimulus kinetics depend on the molecular identity of the odorant and the geometry of the delivery system. We describe methods to deliver dynamic odorant stimuli of several types, including broadly distributed stimuli that reproduce some of the statistics of naturalistic plumes, in a reproducible and precise manner. Finally, we introduce a method to calibrate a photo-ionization detector to any odorant it can detect, using no additional components. Our approaches are affordable and flexible and can be used to advance our understanding of how olfactory neurons encode real-world odor signals.

Odor Stimuli: Not Just Chemical Identity

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals’ physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Slow presynaptic mechanisms that mediate adaptation in the olfactory pathway of Drosophila

The olfactory system encodes odor stimuli as combinatorial activity of populations of neurons whose response depends on stimulus history. How and on which timescales previous stimuli affect these combinatorial representations remains unclear. We use in vivo optical imaging in Drosophila to analyze sensory adaptation at the first synaptic step along the olfactory pathway. We show that calcium signals in the axon terminals of olfactory receptor neurons (ORNs) do not follow the same adaptive properties as the firing activity measured at the antenna. While ORNs calcium responses are sustained on long timescales, calcium signals in the postsynaptic projection neurons (PNs) adapt within tens of seconds. We propose that this slow component of the postsynaptic response is mediated by a slow presynaptic depression of vesicle release and enables the combinatorial population activity of PNs to adjust to the mean and variance of fluctuating odor stimuli.

Neurogenetic dissection of the Drosophila lateral horn reveals major outputs, diverse behavioural functions, and interactions with the mushroom body

Animals exhibit innate behaviours to a variety of sensory stimuli including olfactory cues. In Drosophila, one higher olfactory centre, the lateral horn (LH), is implicated in innate behaviour. However, our structural and functional understanding of the LH is scant, in large part due to a lack of sparse neurogenetic tools for this region. We generate a collection of split-GAL4 driver lines providing genetic access to 82 LH cell types. We use these to create an anatomical and neurotransmitter map of the LH and link this to EM connectomics data. We find ~30% of LH projections converge with outputs from the mushroom body, site of olfactory learning and memory. Using optogenetic activation, we identify LH cell types that drive changes in valence behavior or specific locomotor programs. In summary, we have generated a resource for manipulating and mapping LH neurons, providing new insights into the circuit basis of innate and learned olfactory behavior.

Odor Concentration Change Coding in the Olfactory Bulb

Dynamical changes in the environment strongly impact our perception. Likewise, sensory systems preferentially represent stimulus changes, enhancing temporal contrast. In olfaction, odor concentration changes across consecutive inhalations (ΔC_t) can guide odor source localization, yet the neural representation of ΔC_t has not been studied in vertebrates. We have found that, in the mouse olfactory bulb, a subset of mitral/tufted (M/T) cells represents ΔC_t, enhancing the contrast between different concentrations. These concentration change responses are direction selective: they respond either to increments or decrements of concentration, reminiscent of ON and OFF selectivity in the retina. This contrast enhancement scales with the magnitude, but not the duration of the concentration step. Further, ΔC_t can be read out from the total spike count per sniff, unlike odor identity and intensity, which are represented by fast temporal spike patterns. Our results demonstrate that a subset of M/T cells represents ΔC_t, providing a signal that may instruct navigational decisions in downstream olfactory circuits.

Mechanisms of Sensory Discrimination: Insights from Drosophila Olfaction

All an animal can do to infer the state of its environment is to observe the sensory-evoked activity of its own neurons. These inferences about the presence, quality, or similarity of objects are probabilistic and inform behavioral decisions that are often made in close to real time. Neural systems employ several strategies to facilitate sensory discrimination: Biophysical mechanisms separate the neuronal response distributions in coding space, compress their variances, and combine information from sequential observations. We review how these strategies are implemented in the olfactory system of the fruit fly. The emerging principles of odor discrimination likely apply to other neural circuits of similar architecture.

Odor Stimuli: Not Just Chemical Identity

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals' physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Sensorimotor pathway controlling stopping behavior during chemotaxis in the Drosophila melanogaster larva

Sensory navigation results from coordinated transitions between distinct behavioral programs. During chemotaxis in the Drosophila melanogaster larva, the detection of positive odor gradients extends runs while negative gradients promote stops and turns. This algorithm represents a foundation for the control of sensory navigation across phyla. In the present work, we identified an olfactory descending neuron, PDM-DN, which plays a pivotal role in the organization of stops and turns in response to the detection of graded changes in odor concentrations. Artificial activation of this descending neuron induces deterministic stops followed by the initiation of turning maneuvers through head casts. Using electron microscopy, we reconstructed the main pathway that connects the PDM-DN neuron to the peripheral olfactory system and to the pre-motor circuit responsible for the actuation of forward peristalsis. Our results set the stage for a detailed mechanistic analysis of the sensorimotor conversion of graded olfactory inputs into action selection to perform goal-oriented navigation.

An olfactory virtual reality system for mice

All motile organisms use spatially distributed chemical features of their surroundings to guide their behaviors, but the neural mechanisms underlying such behaviors in mammals have been difficult to study, largely due to the technical challenges of controlling chemical concentrations in space and time during behavioral experiments. To overcome these challenges, we introduce a system to control and maintain an olfactory virtual landscape. This system uses rapid flow controllers and an online predictive algorithm to deliver precise odorant distributions to head-fixed mice as they explore a virtual environment. We establish an odor-guided virtual navigation behavior that engages hippocampal CA1 "place cells" that exhibit similar properties to those previously reported for real and visual virtual environments, demonstrating that navigation based on different sensory modalities recruits a similar cognitive map. This method opens new possibilities for studying the neural mechanisms of olfactory-driven behaviors, multisensory integration, innate valence, and low-dimensional sensory-spatial processing.

Differential Processing by Two Olfactory Subsystems in the Honeybee Brain

Among insects, Hymenoptera present a striking olfactory system with a clear neural dichotomy from the periphery to higher order centers, based on two main tracts of second-order (projection) neurons: the medial and lateral antennal lobe tracts (m-ALT and l-ALT). Despite substantial work on this dual pathway, its exact function is yet unclear. Here, we ask how attributes of odor quality and odor quantity are represented in the projection neurons (PNs) of the two pathways. Using in vivo calcium imaging, we compared the responses of m-ALT and l-ALT PNs of the honey bee Apis mellifera to a panel of 16 aliphatic odorants, and to three chosen odorants at eight concentrations. The results show that each pathway conveys differential information about odorants' chemical features or concentration to higher order centers. While the l-ALT primarily conveys information about odorants' chain length, the m-ALT informs about odorants' functional group. Furthermore, each tract can only predict chemical distances or bees' behavioral responses for odorants that differ according to its main feature, chain length or functional group. Generally l-ALT neurons displayed more graded dose-response relationships than m-ALT neurons, with a correspondingly smoother progression of inter-odor distances with increasing concentration. Comparison of these results with previous data recorded at AL input reveals differential processing by local networks within the two pathways. These results support the existence of parallel processing of odorant features in the insect brain.

Independent processing of increments and decrements in odorant concentration by ON and OFF olfactory receptor neurons

A salient feature of the insect olfactory system is its ability to detect and interpret simultaneously the identity and concentration of an odorant signal along with the temporal stimulus cues that are essential for accurate odorant tracking. The olfactory system of the cockroach utilizes two parallel pathways for encoding of odorant identity and the moment-to-moment succession of odorant concentrations as well as the rate at which concentration changes. This separation originates at the peripheral level of the ORNs (olfactory receptor neurons) which are localized in basiconic and trichoid sensilla. The graded activity of ORNs in the basiconic sensilla provides the variable for the combinatorial representation of odorant identity. The antagonistically responding ON and OFF ORNs in the trichoid sensilla transmit information about concentration increments and decrements with excitatory signals. Each ON and OFF ORN adjusts its gain for odorant concentration and its rate of change to the temporal dynamics of the odorant signal: as the rate of change diminishes, both ORNs improve their sensitivity for the rate of change at the expense of the sensitivity for the instantaneous concentration. This suggests that the ON and OFF ORNs are optimized to detect minute fluctuations or even creeping changes in odorant concentration.

Organization of the Drosophila larval visual circuit

Visual systems transduce, process and transmit light-dependent environmental cues. Computation of visual features depends on photoreceptor neuron types (PR) present, organization of the eye and wiring of the underlying neural circuit. Here, we describe the circuit architecture of the visual system of Drosophila larvae by mapping the synaptic wiring diagram and neurotransmitters. By contacting different targets, the two larval PR-subtypes create two converging pathways potentially underlying the computation of ambient light intensity and temporal light changes already within this first visual processing center. Locally processed visual information then signals via dedicated projection interneurons to higher brain areas including the lateral horn and mushroom body. The stratified structure of the larval optic neuropil (LON) suggests common organizational principles with the adult fly and vertebrate visual systems. The complete synaptic wiring diagram of the LON paves the way to understanding how circuits with reduced numerical complexity control wide ranges of behaviors.

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

Insects find food and mates by navigating odorant plumes that can be highly intermittent, with intensities and durations that vary rapidly over orders of magnitude. Much is known about olfactory responses to pulses and steps, but it remains unclear how olfactory receptor neurons (ORNs) detect the intensity and timing of natural stimuli, where the absence of scale in the signal makes detection a formidable olfactory task. By stimulating Drosophila ORNs in vivo with naturalistic and Gaussian stimuli, we show that ORNs adapt to stimulus mean and variance, and that adaptation and saturation contribute to naturalistic sensing. Mean-dependent gain control followed the Weber-Fechner relation and occurred primarily at odor transduction, while variance-dependent gain control occurred at both transduction and spiking. Transduction and spike generation possessed complementary kinetic properties, that together preserved the timing of odorant encounters in ORN spiking, regardless of intensity. Such scale-invariance could be critical during odor plume navigation.

Head Transcriptomes of Two Closely Related Species of Fruit Flies of the Anastrepha fraterculus Group Reveals Divergent Genes in Species with Extensive Gene Flow

Several fruit flies species of the Anastrepha fraterculus group are of great economic importance for the damage they cause to a variety of fleshy fruits. Some species in this group have diverged recently, with evidence of introgression, showing similar morphological attributes that render their identification difficult, reinforcing the relevance of identifying new molecular markers that may differentiate species. We investigated genes expressed in head tissues from two closely related species: A. obliqua and A. fraterculus, aiming to identify fixed single nucleotide polymorphisms (SNPs) and highly differentiated transcripts, which, considering that these species still experience some level of gene flow, could indicate potential candidate genes involved in their differentiation process. We generated multiple libraries from head tissues of these two species, at different reproductive stages, for both sexes. Our analyses indicate that the de novo transcriptome assemblies are fairly complete. We also produced a hybrid assembly to map each species' reads, and identified 67,470 SNPs in A. fraterculus, 39,252 in A. obliqua, and 6386 that were common to both species. We identified 164 highly differentiated unigenes that had a mean interspecific index ([Formula: see text]) of at least 0.94. We selected unigenes that had Ka/Ks higher than 0.5, or had at least three or more highly differentiated SNPs as potential candidate genes for species differentiation. Among these candidates, we identified proteases, regulators of redox homeostasis, and an odorant-binding protein (Obp99c), among other genes. The head transcriptomes described here enabled the identification of thousands of genes hitherto unavailable for these species, and generated a set of candidate genes that are potentially important to genetically identify species and understand the speciation process in the presence of gene flow of A. obliqua and A. fraterculus.

The wiring diagram of a glomerular olfactory system

The sense of smell enables animals to react to long-distance cues according to learned and innate valences. Here, we have mapped with electron microscopy the complete wiring diagram of the Drosophila larval antennal lobe, an olfactory neuropil similar to the vertebrate olfactory bulb. We found a canonical circuit with uniglomerular projection neurons (uPNs) relaying gain-controlled ORN activity to the mushroom body and the lateral horn. A second, parallel circuit with multiglomerular projection neurons (mPNs) and hierarchically connected local neurons (LNs) selectively integrates multiple ORN signals already at the first synapse. LN-LN synaptic connections putatively implement a bistable gain control mechanism that either computes odor saliency through panglomerular inhibition, or allows some glomeruli to respond to faint aversive odors in the presence of strong appetitive odors. This complete wiring diagram will support experimental and theoretical studies towards bridging the gap between circuits and behavior.

DOI:http://dx.doi.org/10.7554/eLife.14859.001

Our sense of smell can tell us about bread being baked faraway in the kitchen, or whether a leftover piece finally went bad. Similarly to the eyes, the nose enables us to make up a mental image of what lies at a distance. In mammals, the surface of the nose hosts a huge number of olfactory sensory cells, each of which is tuned to respond to a small set of scent molecules. The olfactory sensory cells communicate with a region of the brain called the olfactory bulb. Olfactory sensory cells of the same type converge onto the same small pocket of the olfactory bulb, forming a structure called a glomerulus. Similarly to how the retina generates an image, the combined activity of multiple glomeruli defines an odor.

A particular smell is the combination of many volatile compounds, the odorants. Therefore the interactions between different olfactory glomeruli are important for defining the nature of the perceived odor. Although the types of neurons involved in these interactions were known in insects, fish and mice, a precise wiring diagram of a complete set of glomeruli had not been described. In particular, the points of contact through which neurons communicate with each other – known as synapses – among all the neurons participating in an olfactory system were not known.

Berck, Khandelwal et al. have now taken advantage of the small size of the olfactory system of the larvae of Drosophila fruit flies to fully describe, using high-resolution imaging, all its neurons and their synapses. The results define the complete wiring diagram of the neural circuit that processes the signals sent by olfactory sensory neurons in the larva’s olfactory circuits.

In addition to the neurons that read out the activity of a single glomerulus and send it to higher areas of the brain for further processing, there are also numerous neurons that read out activity from multiple glomeruli. These neurons represent a system, encoded in the genome, for quickly extracting valuable olfactory information and then relaying it to other areas of the brain.

An essential aspect of sensation is the ability to stop noticing a stimulus if it doesn't change. This allows an animal to, for example, find food by moving in a direction that increases the intensity of an odor. Inhibition mediates some aspects of this capability. The discovery of structure in the inhibitory connections among glomeruli, together with prior findings on the inner workings of the olfactory system, enabled Berck, Khandelwal et al. to hypothesize how the olfactory circuits enable odor gradients to be navigated. Further investigation revealed more about how the circuits could detect slight changes in odor concentration regardless of whether the overall odor intensity is strong or faint. And, crucially, it revealed how the worst odors – which can signal danger – can still be perceived in the presence of very strong pleasant odors.

With the wiring diagram, theories about the sense of smell can now be tested using the genetic tools available for Drosophila, leading to an understanding of how neural circuits work.

DOI:http://dx.doi.org/10.7554/eLife.14859.002

Intrinsic and Network Mechanisms Constrain Neural Synchrony in the Moth Antennal Lobe

Projection-neurons (PNs) within the antennal lobe (AL) of the hawkmoth respond vigorously to odor stimulation, with each vigorous response followed by a ~1 s period of suppression—dubbed the “afterhyperpolarization-phase,” or AHP-phase. Prior evidence indicates that this AHP-phase is important for the processing of odors, but the mechanisms underlying this phase and its function remain unknown. We investigate this issue. Beginning with several physiological experiments, we find that pharmacological manipulation of the AL yields surprising results. Specifically, (a) the application of picrotoxin (PTX) lengthens the AHP-phase and reduces PN activity, whereas (b) the application of Bicuculline-methiodide (BIC) reduces the AHP-phase and increases PN activity. These results are curious, as both PTX and BIC are inhibitory-receptor antagonists. To resolve this conundrum, we speculate that perhaps (a) PTX reduces PN activity through a disinhibitory circuit involving a heterogeneous population of local-neurons, and (b) BIC acts to hamper certain intrinsic currents within the PNs that contribute to the AHP-phase. To probe these hypotheses further we build a computational model of the AL and benchmark our model against our experimental observations. We find that, for parameters which satisfy these benchmarks, our model exhibits a particular kind of synchronous activity: namely, “multiple-firing-events” (MFEs). These MFEs are causally-linked sequences of spikes which emerge stochastically, and turn out to have important dynamical consequences for all the experimentally observed phenomena we used as benchmarks. Taking a step back, we extract a few predictions from our computational model pertaining to the real AL: Some predictions deal with the MFEs we expect to see in the real AL, whereas other predictions involve the runaway synchronization that we expect when BIC-application hampers the AHP-phase. By examining the literature we see support for the former, and we perform some additional experiments to confirm the latter. The confirmation of these predictions validates, at least partially, our initial speculation above. We conclude that the AL is poised in a state of high-gain; ready to respond vigorously to even faint stimuli. After each response the AHP-phase functions to prevent runaway synchronization and to “reset” the AL for another odor-specific response.

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules' local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly's entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel's model integration by combining independently developed models of the retina and lamina neuropils in the fly's visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel's ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel's communication performance both over the number of interface ports exposed by an emulation's constituent modules and the total number of modules comprised by an emulation.

Dynamical feature extraction at the sensory periphery guides chemotaxis

Behavioral strategies employed for chemotaxis have been described across phyla, but the sensorimotor basis of this phenomenon has seldom been studied in naturalistic contexts. Here, we examine how signals experienced during free olfactory behaviors are processed by first-order olfactory sensory neurons (OSNs) of the Drosophila larva. We find that OSNs can act as differentiators that transiently normalize stimulus intensity—a property potentially derived from a combination of integral feedback and feed-forward regulation of olfactory transduction. In olfactory virtual reality experiments, we report that high activity levels of the OSN suppress turning, whereas low activity levels facilitate turning. Using a generalized linear model, we explain how peripheral encoding of olfactory stimuli modulates the probability of switching from a run to a turn. Our work clarifies the link between computations carried out at the sensory periphery and action selection underlying navigation in odor gradients.

DOI:http://dx.doi.org/10.7554/eLife.06694.001

Fruit flies are attracted to the smell of rotting fruit, and use it to guide them to nearby food sources. However, this task is made more challenging by the fact that the distribution of scent or odor molecules in the air is constantly changing. Fruit flies therefore need to cope with, and exploit, this variation if they are to use odors as cues.

Odor molecules bind to receptors on the surface of nerve cells called olfactory sensory neurons, and trigger nerve impulses that travel along these cells. While many studies have investigated how fruit flies can distinguish between different odors, less is known about how animals can use variation in the strength of an odor to guide them towards its source.

Optogenetics is a technique that allows neuroscientists to control the activities of individual nerve cells, simply by shining light on to them. Because fruit fly larvae are almost transparent, optogenetics can be used on freely moving animals. Now, Schulze, Gomez-Marin et al. have used optogenetics in these larvae to trigger patterns of activity in individual olfactory sensory neurons that mimic the activity patterns elicited by real odors. These virtual realities were then used to study, in detail, some of the principles that control the sensory navigation of a larva—as it moves using a series of forward ‘runs’ and direction-changing ‘turns’.

Olfactory sensory neurons responded most strongly whenever light levels changed rapidly in strength (which simulated a rapid change in odor concentration). On the other hand, these neurons showed relatively little response to constant light levels (i.e., constant odors). This indicates that the activity of olfactory sensory neurons typically represents the rate of change in the concentration of an odor. An independent study by Kim et al. found that olfactory sensory neurons in adult fruit flies also respond in a similar way.

Schulze, Gomez-Marin et al. went on to show that the signals processed by a single type of olfactory sensory neuron could be used to predict a larva's behavior. Larvae tended to turn less when their olfactory sensory neurons were highly active. Low levels and inhibition of activity in the olfactory sensory neurons had the opposite effect; this promoted turning. It remains to be determined how this relatively simple control principle is implemented by the neural circuits that connect sensory neurons to the parts of a larva's nervous system that are involved with movement.

DOI:http://dx.doi.org/10.7554/eLife.06694.002