Insect Antennal Lobe Research Articles

Optimal routing to cerebellum-like structures.

The vast expansion from mossy fibers to cerebellar granule cells (GrC) produces a neural representation that supports functions including associative and internal model learning. This motif is shared by other cerebellum-like structures and has inspired numerous theoretical models. Less attention has been paid to structures immediately presynaptic to GrC layers, whose architecture can be described as a 'bottleneck' and whose function is not understood. We therefore develop a theory of cerebellum-like structures in conjunction with their afferent pathways that predicts the role of the pontine relay to cerebellum and the glomerular organization of the insect antennal lobe. We highlight a new computational distinction between clustered and distributed neuronal representations that is reflected in the anatomy of these two brain structures. Our theory also reconciles recent observations of correlated GrC activity with theories of nonlinear mixing. More generally, it shows that structured compression followed by random expansion is an efficient architecture for flexible computation.

Neuromodulation and differential learning across mosquito species.

Mosquitoes can change their feeding behaviours based on past experiences, such as shifting from biting animals to biting humans or avoiding defensive hosts (Wolff & Riffell 2018 J. Exp. Biol. 221, jeb157131. (doi:10.1242/jeb.157131)). Dopamine is a critical neuromodulator for insects, allowing flexibility in their feeding preferences, but its role in the primary olfactory centre, the antennal lobe (AL), remains unclear (Vinauger et al. 2018 Curr. Biol. 28, 333-344.e8. (doi:10.1016/j.cub.2017.12.015)). It is also unknown whether mosquitoes can learn some odours and not others, or whether different species learn the same odour cues. We assayed aversive olfactory learning in four mosquito species with different host preferences, and found that they differentially learn odours salient to their preferred host. Mosquitoes that prefer humans learned odours found in mammalian skin, but not a flower odour, and a nectar-feeding species only learned a floral odour. Comparing the brains of these four species revealed significantly different innervation patterns in the AL by dopaminergic neurons. Calcium imaging in the Aedes aegypti AL and three-dimensional image analyses of dopaminergic innervation show that glomeruli tuned to learnable odours have significantly higher dopaminergic innervation. Changes in dopamine expression in the insect AL may be an evolutionary mechanism to adapt olfactory learning circuitry without changing brain structure and confer to mosquitoes an ability to adapt to new hosts.

Experimental and theoretical probe on mechano- and chemosensory integration in the insect antennal lobe.

In nature, olfactory signals are delivered to detectors-for example, insect antennae-by means of turbulent air, which exerts concurrent chemical and mechanical stimulation on the detectors. The antennal lobe, which is traditionally viewed as a chemosensory module, sits downstream of antennal inputs. We review experimental evidence showing that, in addition to being a chemosensory structure, antennal lobe neurons also respond to mechanosensory input in the form of wind speed. Benchmarked with empirical data, we constructed a dynamical model to simulate bimodal integration in the antennal lobe, with model dynamics yielding insights such as a positive correlation between the strength of mechanical input and the capacity to follow high frequency odor pulses, an important task in tracking odor sources. Furthermore, we combine experimental and theoretical results to develop a conceptual framework for viewing the functional significance of sensory integration within the antennal lobe. We formulate the testable hypothesis that the antennal lobe alternates between two distinct dynamical regimes, one which benefits odor plume tracking and one which promotes odor discrimination. We postulate that the strength of mechanical input, which correlates with behavioral contexts such being mid-flight versus hovering near a flower, triggers the transition from one regime to the other.

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.

Comparative Development of the Ant Chemosensory System

The insect antennal lobe (AL) contains the first synapses of the olfactory system, where olfactory sensory neurons (OSNs) contact second-order projection neurons (PNs). In Drosophila melanogaster, OSNs expressing specific receptor genes send stereotyped projections to one or two of about 50 morphologically defined glomeruli [1-3]. The mechanisms for this precise matching between OSNs and PNs have been studied extensively in D.melanogaster, where development is deterministic and independent of neural activity [4-6]. However, a number of insect lineages, most notably the ants, have receptor gene repertoires many times larger than D.melanogaster and exhibit more structurally complex antennal lobes [7-12]. Moreover, perturbation of OSN function via knockout of the odorant receptor (OR) co-receptor, Orco, results in drastic AL reductions in ants [13, 14], but not in Drosophila [15]. Here, we characterize AL development in the clonal raider ant, Ooceraea biroi. We find that, unlike in Drosophila, ORs and Orco are expressed before the onset of glomerulus formation, and Orco protein is trafficked to developing axon terminals, raising the possibility that ORs play a role during ant AL development. Additionally, ablating ant antennae at the onset of pupation results in AL defects that recapitulate the Orco mutant phenotype. Thus, early loss of functional OSN innervation reveals latent structure in the AL that develops independently of peripheral input, suggesting that the AL is initially pre-patterned and later refined in an OSN-dependent manner. This two-step process might increase developmental flexibility and thereby facilitate the rapid evolution and expansion of the ant chemosensory system.

Short term depression, presynaptic inhibition and local neuron diversity play key functional roles in the insect antennal lobe.

As the oldest, but least understood sensory system in evolution, the olfactory system represents one of the most challenging research targets in sensory neurobiology. Although a large number of computational models of the olfactory system have been proposed, they do not account for the diversity in physiology, connectivity of local neurons, and several recent discoveries in the insect antennal lobe, a major olfactory organ in insects. Recent studies revealed that the response of some projection neurons were reduced by application of a GABA antagonist, and that insects are sensitive to odor pulse frequency. To account for these observations, we propose a spiking neural circuit model of the insect antennal lobe. Based on recent anatomical and physiological studies, we included three sub-types of local neurons as well as synaptic short-term depression (STD) in the model and showed that the interaction between STD and local neurons resulted in frequency-sensitive responses. We further discovered that the unexpected response of the projection neurons to the GABA antagonist is the result of complex interactions between STD and presynaptic inhibition, which is required for enhancing sensitivity to odor stimuli. Finally, we found that odor discrimination is improved if the innervation of the local neurons in the glomeruli follows a specific pattern. Our findings suggest that STD, presynaptic inhibition and diverse physiology and connectivity of local neurons are not independent properties, but they interact to play key roles in the function of antennal lobes.

Moth pheromone-selective projection neurons with cell bodies in the antennal lobe lateral cluster exhibit diverse morphological and neurophysiological characteristics.

Olfactory projection neurons convey information from the insect antennal lobe (AL) to higher brain centers. Previous reports have demonstrated that pheromone-responsive projection neurons with cell bodies in the moth medial cell cluster (mcPNs) predominantly have dendritic arborizations in the sexually dimorphic macroglomerular complex (MGC) and send an axon from the AL to the calyces of the mushroom body (CA) as well as the lateral horn (LH) of the protocerebrum via the medial AL tract. These neurons typically exhibit a narrow odor tuning range related to the restriction of their dendritic arbors within a single glomerulus (uniglomerular). In this study, we report on the diverse physiological and morphological properties of a group of pheromone-responsive olfactory projection neurons with cell bodies in the AL lateral cell cluster (MGC lcPNs) of two closely related moth species. All pheromone-responsive lcPNs appeared to exhibit "basket-like" dendritic arborizations in two MGC compartments and made connections with various protocerebral targets including ventrolateral and superior neuropils via projections primarily through the lateral AL tract and to a lesser extent the mediolateral antennal lobe tract. Physiological characterization of MGC lcPNs also revealed a diversity of response profiles including those either enhanced by or reliant upon presentation of a pheromone blend. These responses manifested themselves as higher maximum firing rates and/or improved temporal resolution of pulsatile stimuli. MGC lcPNs therefore participate in conveying diverse olfactory information relating to qualitative and temporal facets of the pheromone stimulus to a more expansive number of protocerebral targets than their mcPN counterparts.

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’.

Spatially resolved time‐frequency analysis of odour coding in the insect antennal lobe

Antennal lobes constitute the first neurophils in the insect brain involved in coding and processing of olfactory information. With their stereotyped functional and anatomical organization, they provide an accessible model with which to investigate information processing of an external stimulus in a neural network invivo. Here, by combining functional calcium imaging with time-frequency analysis, we have been able to monitor the oscillatory components of neural activity upon olfactory stimulation. The aim of this study is to investigate the presence of stimulus-induced oscillatory patterns in the honeybee antennal lobe, and to analyse the distribution of those patterns across the antennal lobe glomeruli. Fast two-photon calcium imaging reveals the presence of low-frequency oscillations, the intensity of which is perturbed by an incoming stimulus. Moreover, analysis of the spatial arrangement of this activity indicates that it is not homogeneous throughout the antennal lobe. On the contrary, each glomerulus displays an odorant-specific time-frequency profile, and acts as a functional unit of the oscillatory activity. The presented approach allows simultaneous recording of complex activity patterns across several nodes of the antennal lobe, providing the means to better understand the network dynamics regulating olfactory coding and leading to perception.

Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb.

The dense reconstruction of neuronal circuits from volumetric electron microscopy (EM) data has the potential to uncover fundamental structure-function relationships in the brain. To address bottlenecks in the workflow of this emerging methodology, we developed a procedure for conductive sample embedding and a pipeline for neuron reconstruction. We reconstructed ∼98% of all neurons (>1,000) in the olfactory bulb of a zebrafish larva with high accuracy and annotated all synapses on subsets of neurons representing different types. The organization of the larval olfactory bulb showed marked differences from that of the adult but similarities to that of the insect antennal lobe. Interneurons comprised multiple types but granule cells were rare. Interglomerular projections of interneurons were complex and bidirectional. Projections were not random but biased toward glomerular groups receiving input from common types of sensory neurons. Hence, the interneuron network in the olfactory bulb exhibits a specific topological organization that is governed by glomerular identity.

Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system

In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and accurate classification requires the inclusion of temporal aspects into the feature set. This investigation therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors and the first 30 s (10%) of the sensors’ continuous response are sufficient to deliver 92% accurate classification without access to an odour onset signal. In contrast to previous approaches, once training is complete, sensor signals can be fed continuously into the classifier without requiring discretization. We conclude that for continuous data there may be a conceptual advantage in using spiking networks, in particular where time is an essential component of computation. Classification was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our group.

Classification of odorants across layers in locust olfactory pathway.

Olfactory processing takes place across multiple layers of neurons from the transduction of odorants in the periphery, to odor quality processing, learning, and decision making in higher olfactory structures. In insects, projection neurons (PNs) in the antennal lobe send odor information to the Kenyon cells (KCs) of the mushroom bodies and lateral horn neurons (LHNs). To examine the odor information content in different structures of the insect brain, antennal lobe, mushroom bodies and lateral horn, we designed a model of the olfactory network based on electrophysiological recordings made in vivo in the locust. We found that populations of all types (PNs, LHNs, and KCs) had lower odor classification error rates than individual cells of any given type. This improvement was quantitatively different from that observed using uniform populations of identical neurons compared with spatially structured population of neurons tuned to different odor features. This result, therefore, reflects an emergent network property. Odor classification improved with increasing stimulus duration: for similar odorants, KC and LHN ensembles reached optimal discrimination within the first 300-500 ms of the odor response. Performance improvement with time was much greater for a population of cells than for individual neurons. We conclude that, for PNs, LHNs, and KCs, ensemble responses are always much more informative than single-cell responses, despite the accumulation of noise along with odor information.

Learning about natural variation of odor mixtures enhances categorization in early olfactory processing.

Natural odors are typically mixtures of several chemical components. Mixtures vary in composition among odor objects that have the same meaning. Therefore a central 'categorization' problem for an animal as it makes decisions about odors in natural contexts is to correctly identify odor variants that have the same meaning and avoid variants that have a different meaning. We propose that identified mechanisms of associative and non-associative plasticity in early sensory processing in the insect antennal lobe and mammalian olfactory bulb are central to solving this problem. Accordingly, this plasticity should work to improve categorization of odors that have the opposite meanings in relation to important events. Using synthetic mixtures designed to mimic natural odor variation among flowers, we studied how honey bees learn about and generalize among floral odors associated with food. We behaviorally conditioned honey bees on a difficult odor discrimination problem using synthetic mixtures that mimic natural variation among snapdragon flowers. We then used calcium imaging to measure responses of projection neurons of the antennal lobe, which is the first synaptic relay of olfactory sensory information in the brain, to study how ensembles of projection neurons change as a result of behavioral conditioning. We show how these ensembles become 'tuned' through plasticity to improve categorization of odors that have the different meanings. We argue that this tuning allows more efficient use of the immense coding space of the antennal lobe and olfactory bulb to solve the categorization problem. Our data point to the need for a better understanding of the 'statistics' of the odor space.

Colocalization of allatotropin and tachykinin-related peptides with classical transmitters in physiologically distinct subtypes of olfactory local interneurons in the cockroach (Periplaneta americana).

In the insect antennal lobe different types of local interneurons mediate complex excitatory and inhibitory interactions between the glomerular pathways to structure the spatiotemporal representation of odors. Mass spectrometric and immunohistochemical studies have shown that in local interneurons classical neurotransmitters are likely to colocalize with a variety of substances that can potentially act as cotransmitters or neuromodulators. In the antennal lobe of the cockroach Periplaneta americana, gamma-aminobutyric acid (GABA) has been identified as the potential inhibitory transmitter of spiking type I local interneurons, whereas acetylcholine is most likely the excitatory transmitter of nonspiking type IIa1 local interneurons. This study used whole-cell patch clamp recordings combined with single-cell labeling and immunohistochemistry to test if the GABAergic type I local interneurons and the cholinergic type IIa1 local interneurons express allatotropin and tachykinin-related neuropeptides (TKRPs). These are two of the most abundant types of peptides in the insect antennal lobe. GABA-like and choline acetyltransferase (ChAT)-like immunoreactivity were used as markers for GABAergic and cholinergic neurons, respectively. About 50% of the GABA-like immunoreactive (-lir) spiking type I local interneurons were allatotropin-lir, and ∼ 40% of these neurons were TKRP-lir. About 20% of nonspiking ChAT-lir type IIa1 local interneurons were TKRP-lir. Our results suggest that in subpopulations of GABAergic and cholinergic local interneurons, allatotropin and TKRPs might act as cotransmitters or neuromodulators. To unequivocally assign neurotransmitters, cotransmitters, and neuromodulators to identified classes of antennal lobe neurons is an important step to deepen our understanding of information processing in the insect olfactory system.

Rapid and slow chemical synaptic interactions of cholinergic projection neurons and GABAergic local interneurons in the insect antennal lobe.

The antennal lobe (AL) of insects constitutes the first synaptic relay and processing center of olfactory information, received from olfactory sensory neurons located on the antennae. Complex synaptic connectivity between olfactory neurons of the AL ultimately determines the spatial and temporal tuning profile of (output) projection neurons to odors. Here we used paired whole-cell patch-clamp recordings in the cockroach Periplaneta americana to characterize synaptic interactions between cholinergic uniglomerular projection neurons (uPNs) and GABAergic local interneurons (LNs), both of which are key components of the insect olfactory system. We found rapid, strong excitatory synaptic connections between uPNs and LNs. This rapid excitatory transmission was blocked by the nicotinic acetylcholine receptor blocker mecamylamine. IPSPs, elicited by synaptic input from a presynaptic LN, were recorded in both uPNs and LNs. IPSPs were composed of both slow, sustained components and fast, transient components which were coincident with presynaptic action potentials. The fast IPSPs were blocked by the GABAA receptor chloride channel blocker picrotoxin, whereas the slow sustained IPSPs were blocked by the GABAB receptor blocker CGP-54626. This is the first study to directly show the predicted dual fast- and slow-inhibitory action of LNs, which was predicted to be key in shaping complex odor responses in the AL of insects. We also provide the first direct characterization of rapid postsynaptic potentials coincident with presynaptic spikes between olfactory processing neurons in the AL.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

Classifying chemical sensor data using GPU-accelerated bio-mimetic neuronal networks based on the insect olfactory system

Chemosensing “e-nose” technology has great potential applications in everyday life, ranging from drug detection to food quality assessment and even the diagnosis of illness. However, odour detection and classification remains a highly challenging domain, characterized by high dimensionality, unknown organization of the vast “odourant space” of volatile chemicals and complex, turbulent odour plumes. To compound these difficulties, current sensor technology continues to exhibit distinct shortcomings in speed, sensitivity, selectivity, recovery, and drift avoidance. In the research reported here we turn to a range of recent neuronal models [1-6] that were developed to describe the insect olfactory system, and have been shown to perform well across a range of classic, static classification tasks such as the MNIST handwritten digit set and the Sigma-Aldrich scent database. The insect olfactory system has been extensively studied and has been shown to be both fast and highly effective in complex natural conditions despite its limited size and complexity (when compared to the mammalian system) [2]. Insects such as moths, honey bees, locusts and fruit flies are capable of odour detection and classification tasks well beyond the abilities of current e-nose technology and machine learning algorithms [6]. We present results of applying an insect-inspired approach to the design of a learning spiking neural network that receives synchronized time series data from up to 12 metal-oxide based gas sensors, comprising an optimised [7] combination of classical doped tin oxide and novel zeolite-coated chromium titanium oxide sensors. We have collected sample data sets for classification tasks ranging from “easy” (single chemical identification presented under laboratory conditions) through to “hard” (identification of indicators of infectious diseases in breath samples taken from patients). To address slow sensor response we consider a range of transient-based processing before applying self-organisation techniques to most effectively locate “virtual receptors” (VR) in sensor space. We look to address decorrelation (separation in feature space) and the supervised association of responses with rewards by using correlates of the insect antennal lobe (AL) and mushroom body (MB) structures whilst applying reward-based spike-timing dependent plasticity mechanisms. Classification accuracy is compared with support vector machine (SVM) learning which we also look to match for speed through the use of GPU accelerated neural simulation [5] via the NVidia CUDA TM -based GeNN platform (http://sourceforge.net/projects/genn/).

Multiple roles of inhibition in the olfactory system

Olfactory stimuli are vast in number and defy simple description. In contrast to light or sound, no low dimensional basis suffices to represent chemical stimuli. And yet, the olfactory system maps the complex and highdimensional world of chemical stimuli into unique and reproducible ensembles of neuronal activity. This mapping includes multilevel processing and involves complex strategies for information encoding. In the insect, olfactory receptor neurons (ORNs) synapse onto a smaller group of excitatory projection neurons (PNs) and inhibitory local neurons (LNs) in the antennal lobe (AL). The odor information is then transferred to the mushroom body (MB), a structure analogous to the olfactory cortex and where olfactory memories are formed. Inhibitory circuits of the AL and the MB shape the odor representation as it progresses through the olfactory system. Drawing on results obtained with biophysical network models, I will discuss the role played by feedback and feedforward inhibition in shaping odor-driven responses of olfactory neurons. Using model of the locust olfactory system we described the role of the fast feedback GABAa-type inhibition in generating odor triggered AL oscillations. Our study suggested that the transient nature of PN synchronization could be explained by variations in inhibitory drive from inhibitory LNs to specific groups of PNs over the duration of the odor-elicited response and we established a relationship between an important structural property of an inhibitory AL network, its colorings, and the dynamics it constrains. We showed that our description allows the explicit identification of the groups of inhibitory interneurons in the insect AL that switch, during odor stimulation, between activity and quiescence to provide transiently synchronized response of the PNs. This description optimally matches the perspective of the downstream KCs looking for synchrony in ensembles of pre–synaptic cells. We further found that feedback inhibition provided by the giant GABAergic neuron (GGN) is sufficient to maintain sparse but varied excitation in the KCs across range of odor concentrations. Our study revealed how multiple forms of synaptic inhibition differentially contribute to process information in the olfactory system.

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.

Stereotopy versus stochasticity in olfaction

Although projections from the insect antennal lobe to the mushroom body are probabilistic, those to the lateral horn are stereotyped, suggesting an interplay of preconfigured and plastic circuits in olfactory processing.