Behavioural correlates of combinatorial versus temporal features of odour codes

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Serotonergic amplification of odor-evoked neural responses maps onto flexible behavioral outcomes

Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts (Schistocerca americana). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses.

Synergistic olfactory processing for social plasticity in desert locusts

Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic (solitarious) to swarming (gregarious). Here, we elucidate the implications of this transition on foraging decisions and corresponding neural circuits. We use behavioral experiments and Bayesian modeling to decompose the multi-modal facets of foraging, revealing olfactory social cues as critical. To this end, we investigate how corresponding odors are encoded in the locust olfactory system using in-vivo calcium imaging. We discover crowding-dependent synergistic interactions between food-related and social odors distributed across stable combinatorial response maps. The observed synergy was specific to the gregarious phase and manifested in distinct odor response motifs. Our results suggest a crowding-induced modulation of the locust olfactory system that enhances food detection in swarms. Overall, we demonstrate how linking sensory adaptations to behaviorally relevant tasks can improve our understanding of social modulation in non-model organisms.

Neural manifolds for odor-driven innate and acquired appetitive preferences

Sensory stimuli evoke spiking neural responses that innately or after learning drive suitable behavioral outputs. How are these spiking activities intrinsically patterned to encode for innate preferences, and could the neural response organization impose constraints on learning? We examined this issue in the locust olfactory system. Using a diverse odor panel, we found that ensemble activities both during ('ON response') and after stimulus presentations ('OFF response') could be linearly mapped onto overall appetitive preference indices. Although diverse, ON and OFF response patterns generated by innately appetitive odorants (higher palp-opening responses) were still limited to a low-dimensional subspace (a 'neural manifold'). Similarly, innately non-appetitive odorants evoked responses that were separable yet confined to another neural manifold. Notably, only odorants that evoked neural response excursions in the appetitive manifold could be associated with gustatory reward. In sum, these results provide insights into how encoding for innate preferences can also impact associative learning.

Conserved neural dynamics and computations across species in olfaction

Interpreting chemical information and translating it into ethologically relevant output is a common challenge of olfactory systems across species. Are computations performed by olfactory circuits conserved across species to overcome these common challenges? To understand this, we compared odor responses in the locust antennal lobe (AL) and mouse olfactory bulb (OB). We found that odors activated nearly mutually exclusive neural ensembles during stimulus presentation ('ON response') and after stimulus termination ('OFF response'). Strikingly, ON and OFF responses evoked by a single odor were anticorrelated with each other. 'Inverted' OFF responses enhanced contrast between odors experienced close together in time. Notably, OFF responses persisted long after odor termination in both AL and OB networks, indicating a form of short-term memory. Taken together, our results reveal key neurodynamic features underlying olfactory computations that are conserved across insect and mammalian olfactory systems.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76-100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Invariant odor recognition with ON-OFF neural ensembles

The smell of coffee is the same whether it is smelled in a coffee shop or grocery shop (different backgrounds), on a hot day or a cold day (different ambient conditions), after lunch or dinner (different temporal contexts), or using a deep inhalation or normal inhalation (different stimulus dynamics). This feat of pattern recognition that is still difficult to achieve in artificial chemical sensing systems is performed by most sensory systems for their survival. How is this capability achieved? We explored this issue. We found that there are two orthogonal ensembles of neurons, one activated during stimulus presence (ON neurons) and one activated after its termination (OFF neurons), and both contribute to this important computation in a complementary fashion.

Invariant stimulus recognition is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus are perturbed in a multitude of ways, how can this computational capability be achieved? We examine this issue in the locust olfactory system. We find that locusts trained in an appetitive-conditioning assay robustly recognize the trained odorant independent of variations in stimulus durations, dynamics, or history, or changes in background and ambient conditions. However, individual- and population-level neural responses vary unpredictably with many of these variations. Our results indicate that linear statistical decoding schemes, which assign positive weights to ON neurons and negative weights to OFF neurons, resolve this apparent confound between neural variability and behavioral stability. Furthermore, simplification of the decoder using only ternary weights ({+1, 0, −1}) (i.e., an “ON-minus-OFF” approach) does not compromise performance, thereby striking a fine balance between simplicity and robustness.

Olfactory encoding within the insect antennal lobe: The emergence and role of higher order temporal correlations in the dynamics of antennal lobe spiking activity

In this review, we focus on the antennal lobe (AL) of three insect species - the fruit fly, sphinx moth, and locust. We first review the experimentally elucidated anatomy and physiology of the early olfactory system of each species; empirical studies of AL activity, however, often focus on assessing firing rates (averaged over time scales of about 100 ms), and hence the AL odor code is often analyzed in terms of a temporally evolving vector of firing rates. However, such a perspective necessarily misses the possibility of higher order temporal correlations in spiking activity within a single cell and across multiple cells over shorter time scales (of about 10 ms). Hence, we then review our prior theoretical work, where we constructed biophysically detailed, species-specific AL models within the fly, moth, and locust, finding that in each case higher order temporal correlations in spiking naturally emerge from model dynamics (i.e., without a prioriincorporation of elements designed to produce correlated activity). We therefore use our theoretical work to argue the perspective that temporal correlations in spiking over short time scales, which have received little experimental attention to-date, may provide valuable coding dimensions (complementing the coding dimensions provided by the vector of firing rates) that nature has exploited in the encoding of odors within the AL. We further argue that, if the AL does indeed utilize temporally correlated activity to represent odor information, such an odor code could be naturally and easily deciphered within the Mushroom Body.

Dynamic contrast enhancement and flexible odor codes

Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here we examine this issue in the locust olfactory system. In the antennal lobe, we find that both spatial and temporal features of odor-evoked responses vary in a stimulus-history dependent manner. The response variations are not random, but allow the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity is conf ounded due to this contrast enhancement computation. Notably, predictions from a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons match results from behavioral experiments. In sum, our results suggest that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic.

Sensory stimuli are encountered in multiple ways necessitating a flexible and adaptive neural population code for identification. Here, the authors show that the dynamics of odor coding in the locust antennal lobe varies with stimulus context so as to enhance the target stimulus representation.

Differential effects of adaptation on odor discrimination

Adaptation of neural responses is ubiquitous in sensory systems and can potentially facilitate many important computational functions. Here we examined this issue with a well-constrained computational model of the early olfactory circuits. In the insect olfactory system, the responses of olfactory receptor neurons (ORNs) on the antennae adapt over time. We found that strong adaptation of sensory input is important for rapidly detecting a fresher stimulus encountered in the presence of other background cues and for faithfully representing its identity. However, when the overlapping odorants were chemically similar, we found that adaptation could alter the representation of these odorants to emphasize only distinguishing features. This work demonstrates novel roles for peripheral neurons during olfactory processing in complex environments. NEW & NOTEWORTHY Olfactory systems face the problem of distinguishing salient information from a complex olfactory environment. The neural representations of specific odor sources should be consistent regardless of the background. How are olfactory representations robust to varying environmental interference? We show that in locusts the extraction of salient information begins in the periphery. Olfactory receptor neurons adapt in response to odorants. Adaptation can provide a computational mechanism allowing novel odorant components to be highlighted during complex stimuli.

Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex

The notion that neurons with higher selectivity carry more information about external sensory inputs is widely accepted in neuroscience. High-selectivity neurons respond to a narrow range of sensory inputs, and thus would be considered highly informative by rejecting a large proportion of possible inputs. In auditory cortex, neuronal responses are less selective immediately after the onset of a sound and then become highly selective in the following sustained response epoch. These 2 temporal response epochs have thus been interpreted to encode first the presence and then the content of a sound input. Contrary to predictions from that prevailing theory, however, we found that the neural population conveys similar information about sound input across the 2 epochs in spite of the neuronal selectivity differences. The amount of information encoded turns out to be almost completely dependent upon the total number of population spikes in the read-out window for this system. Moreover, inhomogeneous Poisson spiking behavior is sufficient to account for this property. These results imply a novel principle of sensory encoding that is potentially shared widely among multiple sensory systems.

Two Olfactory Pathways to Detect Aldehydes on Locust Mouthpart

Sensing chemical cues is crucial for insects through their olfactory systems to adapt the environments. The receptors employed in insect olfactory system belong to the Odorant Receptor (ORs) and Ionotropic Receptor (IRs) families. In general, ORs and IRs are present in distinct olfactory sensory neurons and function independently. Here, we present evidence that in locust, the abundant host plant odor Hexanal is detected by both IR- and OR-expressing neurons. Use of the palp opening response (POR) as a simple behavioral paradigm in conjunction with RNA interference (RNAi) revealed that these two pathways are both needed for the detection of Hexanal. Two-color fluorescence in situ hybridization showed that OR2 and odorant-binding protein 1 (obp1) were co-localized in palps sensilla basiconica. Obp2a and IR8a were co-localized as well, but associated with sensilla chaetica on the palps. Furthermore, both OR2- and obp1-knockdowns showed reduced POR responses to Hexanal and E-2-Hexenal, and the same was true for Hexanal with IR8a- and obp2a-knockdowns. Detection to E-2-Hexenal was independent of IR8a-mediated gene silencing. Besides, Hexanal and E-2-Hexenal evoked dose-dependent responses in palp basiconica via extracellular recordings. Our results indicate that both OR and IR pathways are involved in the detection of one aldehyde.

Engaging and disengaging recurrent inhibition coincides with sensing and unsensing of a sensory stimulus

Even simple sensory stimuli evoke neural responses that are dynamic and complex. Are the temporally patterned neural activities important for controlling the behavioral output? Here, we investigated this issue. Our results reveal that in the insect antennal lobe, due to circuit interactions, distinct neural ensembles are activated during and immediately following the termination of every odorant. Such non-overlapping response patterns are not observed even when the stimulus intensity or identities were changed. In addition, we find that ON and OFF ensemble neural activities differ in their ability to recruit recurrent inhibition, entrain field-potential oscillations and more importantly in their relevance to behaviour (initiate versus reset conditioned responses). Notably, we find that a strikingly similar strategy is also used for encoding sound onsets and offsets in the marmoset auditory cortex. In sum, our results suggest a general approach where recurrent inhibition is associated with stimulus ‘recognition' and ‘derecognition'.

Sensory stimuli evoke temporally dynamic responses. Here the authors report that responses to odour onset and offset are orthogonally represented in the locust antennal lobe, differentially entrain oscillations, and propose a model in which they are necessary for initiation and termination of behaviour.

Non-invasive aerosol delivery and transport of gold nanoparticles to the brain

Targeted delivery of nanoscale carriers containing packaged payloads to the central nervous system has potential use in many diagnostic and therapeutic applications. Moreover, understanding of the bio-interactions of the engineered nanoparticles used for tissue-specific delivery by non-invasive delivery approaches are also of paramount interest. Here, we have examined this issue systematically in a relatively simple invertebrate model using insects. We synthesized 5 nm, positively charged gold nanoparticles (AuNPs) and targeted their delivery using the electrospray aerosol generator. Our results revealed that after the exposure of synthesized aerosol to the insect antenna, AuNPs reached the brain within an hour. Nanoparticle accumulation in the brain increased linearly with the exposure time. Notably, electrophysiological recordings from neurons in the insect brain several hours after exposure did not show any significant alterations in their spontaneous and odor-evoked spiking properties. Taken together, our findings reveal that aerosolized delivery of nanoparticles can be an effective non-invasive approach for delivering nanoparticles to the brain, and also presents an approach to monitor the short-term nano-biointeractions.

The I/O transform of a chemical sensor

A number of sensing technologies, using a variety of transduction principles, have been proposed for non-invasive chemical sensing. A fundamental problem common to all these sensing technologies is determining what features of the transducer's signal constitute a chemical fingerprint that allows for precise analyte recognition. Of particular importance is the need to extract features that are robust with respect to the sensor's age or stimulus intensity. Here, using pulsed stimulus delivery, we show that a sensor's operation can be modeled as a linear input-output (I/O) transform. The I/O transform is unique for each analyte and can be used to precisely predict a temperature-programmed chemiresistor's response to the analyte given the recent stimulus history (i.e. state of an analyte delivery valve being open or closed). We show that the analyte specific I/O transforms are to a certain degree stimulus intensity invariant and can remain consistent even when the sensor has undergone considerable aging. Significantly, the I/O transforms for a given analyte are highly conserved across sensors of equal manufacture, thereby allowing training data obtained from one sensor to be used for recognition of the same set of chemical species with another sensor. Hence, this proposed approach facilitates decoupling of the signal processing algorithms from the chemical transducer, a key advance necessary for achieving long-term, non-invasive chemical sensing.

Environmental Adaptation, Phenotypic Plasticity, and Associative Learning in Insects: The Desert Locust as a Case Study

The ability to learn and store information should be adapted to the environment in which animals operate to confer a selective advantage. Yet the relationship between learning, memory, and the environment is poorly understood, and further complicated by phenotypic plasticity caused by the very environment in which learning and memory need to operate. Many insect species show polyphenism, an extreme form of phenotypic plasticity, allowing them to occupy distinct environments by producing two or more alternative phenotypes. Yet how the learning and memories capabilities of these alternative phenotypes are adapted to their specific environments remains unknown for most polyphenic insect species. The desert locust can exist as one of two extreme phenotypes or phases, solitarious and gregarious. Recent studies of associative food-odor learning in this locust have shown that aversive but not appetitive learning differs between phases. Furthermore, switching from the solitarious to the gregarious phase (gregarization) prevents locusts acquiring new learned aversions, enabling them to convert an aversive memory formed in the solitarious phase to an appetitive one in the gregarious phase. This conversion provides a neuroecological mechanism that matches key changes in the behavioral environments of the two phases. These findings emphasize the importance of understanding the neural mechanisms that generate ecologically relevant behaviors and the interactions between different forms of behavioral plasticity.