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Invariant recognition of a stimulus is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus could be perturbed in a multitude of ways, could a single scheme be devised to achieve this computational capability? We examined this issue in locust olfactory system. We found that odor-evoked responses in individual projection neurons in the locust antennal lobe varied unpredictably with repetition, stimulus dynamics, stimulus history, presence of background odorants, and changes in ambient conditions. Yet, a highly-constrained Bayesian logistic regression approach with ternary weights could provide robust odor recognition. We found that this approach could be further simplified: sum firing rates of ON neurons and subtract total activity in OFF neurons (‘ON minus OFF’ classifier). Notably, we found that this approach could be generalized to develop a Boolean neural network that can perform well in a non-olfactory pattern recognition task.

Current evidence suggests that malarial infection could alter metabolites in the breath of patients, a phenomenon that could be exploited to create a breath-based diagnostic test. However, no study has explored this in a clinical setting. To investigate whether natural human malarial infection leads to a characteristic breath profile, we performed a field study in Malawi. Breath volatiles from children with and those without uncomplicated falciparum malaria were analyzed by thermal desorption-gas chromatography/mass spectrometry. Using an unbiased, correlation-based analysis, we found that children with malaria have a distinct shift in overall breath composition. Highly accurate classification of infection status was achieved with a suite of 6 compounds. In addition, we found that infection correlates with significantly higher breath levels of 2 mosquito-attractant terpenes, α-pinene and 3-carene. These findings attest to the viability of breath analysis for malaria diagnosis, identify candidate biomarkers, and identify plausible chemical mediators for increased mosquito attraction to patients infected with malaria parasites.

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.

Functional properties of neurons are often distributed nonrandomly within a cortical area and form topographic maps that reveal insights into neuronal organization and interconnection. Some functional maps, such as in visual cortex, are fairly straightforward to discern with a variety of techniques, while other maps, such as in auditory cortex, have resisted easy characterization. In order to determine appropriate protocols for establishing accurate functional maps in auditory cortex, artificial topographic maps were probed under various conditions, and the accuracy of estimates formed from the actual maps was quantified. Under these conditions, low-complexity maps such as sound frequency can be estimated accurately with as few as 25 total samples (e.g., electrode penetrations or imaging pixels) if neural responses are averaged together. More samples are required to achieve the highest estimation accuracy for higher complexity maps, and averaging improves map estimate accuracy even more than increasing sampling density. Undersampling without averaging can result in misleading map estimates, while undersampling with averaging can lead to the false conclusion of no map when one actually exists. Uniform sample spacing only slightly improves map estimation over nonuniform sample spacing typical of serial electrode penetrations. Tessellation plots commonly used to visualize maps estimated using nonuniform sampling are always inferior to linearly interpolated estimates, although differences are slight at higher sampling densities. Within primary auditory cortex, then, multiunit sampling with at least 100 samples would likely result in reasonable feature map estimates for all but the highest complexity maps and the highest variability that might be expected.

Most sensory stimuli evoke spiking responses that are distributed across neurons and are temporally structured. Whether the temporal structure of ensemble activity is modulated to facilitate different neural computations is not known. Here, we investigated this issue in the insect olfactory system. We found that an odourant can generate synchronous or asynchronous spiking activity across a neural ensemble in the antennal lobe circuit depending on its relative novelty with respect to a preceding stimulus. Regardless of variations in temporal spiking patterns, the activated combinations of neurons robustly represented stimulus identity. Consistent with this interpretation, locusts reliably recognized both solitary and sequential introductions of trained odourants in a quantitative behavioural assay. However, predictable behavioural responses across locusts were observed only to novel stimuli that evoked synchronized spiking patterns across neural ensembles. Hence, our results indicate that the combinatorial ensemble response encodes for stimulus identity, whereas the temporal structure of the ensemble response selectively emphasizes novel stimuli., In the olfactory system, odourants typically evoke spiking responses in neurons that are both spatially and temporally structured. Here, the authors demonstrate that odour identity is encoded purely by the combinations of neurons activated and is insensitive to changes in temporal structure.

Here, we report our findings on the characterization of olfactory receptor neuron (ORN) responses in the insect antenna to various analytes and their combinations. We investigated the relationship between sensory neuron responses to a binary odor mixture with their responses to the component cues. We observed that sensory neuron responses to stimulus blends resulted in simple combinations of component activities. Not only spike counts, but also temporal response motifs evoked by component odors were combined. We then examined how a binary mixture response relates to the component signals in a miniaturized chemical sensor array. The sensor array comprised of two different sensing films: reduced graphene oxide and gold nanoparticle chains functionalized with p-aminothiophenol. Our results reveal that several response properties observed in various types of chemical sensors are strikingly similar. Hence, these results provide an important foundation to develop a truly neuromorphic chemical sensing system.

Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time1. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons)2,3. The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning4,5. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits. First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs6,7. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode3. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings8. We provide details of our experimental setup and present representative recording traces for each of these techniques.