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

A Behavioral Odor Similarity “Space” in Larval Drosophila

To provide a behavior-based estimate of odor similarity in larval Drosophila, we use 4 recognition-type experiments: 1) We train larvae to associate an odor with food and then test whether they would regard another odor as the same as the trained one. 2) We train larvae to associate an odor with food and test whether they prefer the trained odor against a novel nontrained one. 3) We train larvae differentially to associate one odor with food, but not the other one, and test whether they prefer the rewarded against the nonrewarded odor. 4) In an experiment like (3), we test the larvae after a 30-min break. This yields a combined task-independent estimate of perceived difference between odor pairs. Comparing these perceived differences to published measures of physicochemical difference reveals a weak correlation. A notable exception are 3-octanol and benzaldehyde, which are distinct in published accounts of chemical similarity and in terms of their published sensory representation but nevertheless are consistently regarded as the most similar of the 10 odor pairs employed. It thus appears as if at least some aspects of olfactory perception are “computed” in postreceptor circuits on the basis of sensory signals rather than being immediately given by them

Morphology, phylogeny, and taxonomy of Microthlaspi (Brassicaceae: Coluteocarpeae) and related genera

The genus Thlaspi has been variously subdivided since its description by Linnaeus in 1753, but due to similarities in fruit shape several segregates have still not gained broad recognition, despite the fact that they are not directly related to Thlaspi. This applies especially to segregates now considered to belong to the tribe Coluteocarpeae, which includes several well-studied taxa, e.g., Noccaea caerulescens (syn. Thlaspi caerulescens), and the widespread Microthlaspi perfoliatum (syn. Thlaspi perfoliatum). The taxonomy of this tribe is still debated, as a series of detailed monographs on Coluteocarpeae was not published in English and a lack of phylogenetic resolution within this tribe was found in previous studies. The current study presents detailed phylogenetic investigations and a critical review of morphological features, with focus on taxa previously placed in Microthlaspi. Based on one nuclear (ITS) and two chloroplast (matK, trnL-F) loci, four strongly supported major groups were recovered among the Coluteocarpeae genera included, corresponding to Ihsanalshehbazia gen. nov., Friedrichkarlmeyeria gen. nov., Microthlaspi s.str., and Noccaea s.l. In addition, two new species of Microthlaspi, M. sylvarum-cedri sp. nov. and M. mediterraneo-orientale sp. nov., were discovered, which are well supported by both morphological and molecular data. Furthermore, M. erraticum comb. nov. (diploid) and M. perfoliatum s.str. (polyploid) were shown to be distinct species, phylogenetically widely separate, but with some overlap in several morphological characters. Detailed descriptions, notes on taxonomy, geographical distribution, and line drawings for the new species and each species previously included in Microthlaspi are provided. In addition, the current taxonomic state of the tribe Coluteocarpeae is briefly discussed and it is concluded that while several annual taxa are clearly distinct from Noccaea, many perennial taxa, after thorough phylogenetic and morphological investigations, may have to be merged with this genus. © International Association for Plant Taxonomy (IAPT) 2016

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map

© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Progress in olfactory research is currently hampered by incomplete knowledge about chemical receptive ranges of primary receptors. Moreover, the chemical logic underlying the arrangement of computational units in the olfactory bulb has still not been resolved. We undertook a large-scale approach at characterising molecular receptive ranges (MRRs) of glomeruli in the dorsal olfactory bulb (dOB) innervated by the MOR18-2 olfactory receptor, also known as Olfr78, with human ortholog OR51E2. Guided by an iterative approach that combined biological screening and machine learning, we selected 214 odorants to characterise the response of MOR18-2 and its neighbouring glomeruli. We found that a combination of conventional physico-chemical and vibrational molecular descriptors performed best in predicting glomerular responses using nonlinear Support-Vector Regression. We also discovered several previously unknown odorants activating MOR18-2 glomeruli, and obtained detailed MRRs of MOR18-2 glomeruli and their neighbours. Our results confirm earlier findings that demonstrated tunotopy, that is, glomeruli with similar tuning curves tend to be located in spatial proximity in the dOB. In addition, our results indicate chemotopy, that is, a preference for glomeruli with similar physico-chemical MRR descriptions being located in spatial proximity. Together, these findings suggest the existence of a partial chemical map underlying glomerular arrangement in the dOB. Our methodology that combines machine learning and physiological measurements lights the way towards future high-throughput studies to deorphanise and characterise structure-activity relationships in olfaction.Peer reviewe

A Combined Perceptual, Physico-Chemical, and Imaging Approach to ‘Odour-Distances’ Suggests a Categorizing Function of the Drosophila Antennal Lobe

How do physico-chemical stimulus features, perception, and physiology relate? Given the multi-layered and parallel architecture of brains, the question specifically is where physiological activity patterns correspond to stimulus features and/or perception. Perceived distances between six odour pairs are defined behaviourally from four independent odour recognition tasks. We find that, in register with the physico-chemical distances of these odours, perceived distances for 3-octanol and n-amylacetate are consistently smallest in all four tasks, while the other five odour pairs are about equally distinct. Optical imaging in the antennal lobe, using a calcium sensor transgenically expressed in only first-order sensory or only second-order olfactory projection neurons, reveals that 3-octanol and n-amylacetate are distinctly represented in sensory neurons, but appear merged in projection neurons. These results may suggest that within-antennal lobe processing funnels sensory signals into behaviourally meaningful categories, in register with the physico-chemical relatedness of the odours

The Plasmodium Export Element Revisited

We performed a bioinformatical analysis of protein export elements (PEXEL) in the putative proteome of the malaria parasite Plasmodium falciparum. A protein family-specific conservation of physicochemical residue profiles was found for PEXEL-flanking sequence regions. We demonstrate that the family members can be clustered based on the flanking regions only and display characteristic hydrophobicity patterns. This raises the possibility that the flanking regions may contain additional information for a family-specific role of PEXEL. We further show that signal peptide cleavage results in a positional alignment of PEXEL from both proteins with, and without, a signal peptide

Predicting olfactory receptor neuron responses from odorant structure

Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusions The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their 'receptive fields'. Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022