Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Self-organization in the olfactory system: one shot odor recognition in insects

We show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Sensor-based machine olfaction with neuromorphic models of the olfactory system

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings

Mimicking the human olfactory system: a portable e-­mucosa

The study of electronic noses has been an active area of research for over 25 years. Commercial instruments have been successfully deployed within niche application areas, for example, the food, beverage and pharmaceutical industries. However, these instruments are still inferior to their human counterparts and have not achieved mainstream success. Humans can distinguish and identify many thousands of different aromas, even at very low concentration levels, with relative ease. The human olfactory system is extremely sophisticated, which allows it to out-­perform artificial instruments. Though limited, artificial instruments can provide a lower cost option to specific problems and can be an alternative to the use of organoleptic panels. Most existing commercial electronic nose (e-­nose) instruments are expensive, bulky, desktop units, requiring a PC to operate. In addition, these instruments usually require a trained operator to gather and analyse the data. Motivated to improve the performance, size and cost of e-­nose instruments, this research aims to extract biological principles from the mammalian olfactory system to aid the implementation of a portable e-­nose instrument. This study has focused on several features of the biological system that may provide the key to its superior performance. Specifically, the large number of different olfactory receptors and the diversity of these receptors; the nasal chromatograph effect; stereo olfaction; sniff rate and odour conditioning. Based on these features, a novel, portable, cost effective instrument, called the Portable e-­Mucosa (PeM), has been designed, implemented and tested. The main components of the PeM are three sensor arrays each containing 200 carbon black composite chemoresistive sensors (totalling 600 sensors with 24 different tunings) mimicking the large number of olfactory receptors and two gas chromatographic columns (coated with non-­polar and polar compounds to maximise the discrimination) emulating the “nasal chromatograph” effect of the human mucus. A preconcentrator based on thermal desorption is also included as an odour collection system to further improve the instrument. The PeM provides USB and Multimedia Memory Card support for easy communication with a PC. The instrument weighs 700g and, with dimensions of 110 x 210 x 110 mm, is slightly larger than the commercial Cyranose 320 (produced by Smiths Detection). This novel instrument generates ‘spatio-­temporal’ data and when coupled with an appropriate pattern recognition algorithm, has shown an enhanced ability to discriminate between odours. The instrument successfully discriminates between simple odours (ethanol, ethyl acetate and acetone) and more complex odours (lavender, ylang ylang, cinnamon and lemon grass essential oils). This system can perhaps be seen as a foundation for a new generation of e-noses

Pharmacological analysis of ionotropic glutamate and GABA recptor function in neuronal circuits of the zebrafish olfactory bulb

In the olfactory bulb and other brain areas, basic cellular and synaptic properties of individual neurons have been studied extensively in reduced preparations. Nevertheless, it is still poorly understood how intactions between multiple neurons shape spatio-temporal activity patterns and give rise to the computational properties of the the intact circuit. In this thesis, I used pharmacological manipulations of excitatory and inhibitory neurotransmitter receptors to examine the synaptic interactions underlying spontaneous and odor-evoked activity patterns in the intact olfactory bulb of zebrafish. Electrophysiological and one- and two-photon calcium imaging methods were used to record activity from the principal neurons of the OB (mitral cells, MCs), their sensory input, and local interneurons. The combined blockade of AMPA/kainate and NMDA receptors abolished odor-evoked excitation of MCs, indicating that sensory input to the OB is mediated by ionotropic glutamate receptors. Surprisingly, however, the blockade of AMPA/Kainiate receptors alone increased the mean response of MCs and decreased the mean response of interneurons (INs), and the blockade of NMDA receptors caused little or no change in the mean responses of MCs and INs. In addition, antagonists of both glutamate receptor types had diverse effects on the magnitude and time course of individual MC and IN responses and, thus, changed spatio-temporal activity patterns across neuronal populations. The blockade of GABA(A) receptors increased spontaneous and odor evoked firing rates of mitral cells and often induced rhythmic bursting. Moreover, the blockade of, GABA(A) or AMPA/kainate receptors abolished fast oscillatory activity in the local field potential. Blockade of GABA(B) receptors reduced calcium influx in afferent sensory axons and modulated response time courses of mitral cells. These results indicate that (1) IN activity during an odor response depends mainly on AMPA/Kainiate receptor input, (2) interactions between MCs and INs regulate the total OB output activity, (3) AMPA/Kainiate receptors and GABA(A) receptors underly the synchronization of odor-dependent neuronal ensembles and (4) odor-specific patterns of OB output activity are shaped by circuits containing iGlu receptors and GABA receptors. These results provide insights into the mechanisms underlying the processing of odor-encoding activity patterns in the OB.Im olfaktorischen Bulbus (OB) und anderen Hirnarealen wurden grundlegende zelluläre und synaptische Eigenschaften der Einzelneurone ausführlich in reduzierten Präparaten studiert. Trotzdem ist kaum bekannt, wie die Interaktionen mehrerer Nervenzellen untereinander räumlich-zeitlich strukturierte Aktivitätsmuster formen und dadurch die rechnerischen Eigenschaften der intakten Schaltkreise entstehen. In dieser Arbeit nutzte ich pharmakologische Manipulationen der erregenden und hemmenden Neurotransmitter-Rezeptoren, um die synaptischen Interaktionen zu untersuchen, die spontanen und geruchsinduzierten Aktivitätsmustern im intakten OB des Zebrafisch zugrunde liegen. Methoden der Elektrophysiology sowie der konventionellen und Zwei-Photonen-Mikroskopie wurden genutzt, um Aktivität von Ausgangsneuronen des OB (Mitralzellen, MCs), ihrem sensorischen Eingang, und Interneuronen (INs) zu messen. Die gleichzeitige Blockierung von AMPA/Kainate- und NMDA-Rezeptoren verhinderte die geruchsinduzierte Erregung von MCs, was darauf hinweist, dass der sensorische Eingang des OB durch ionotrope Glutamatrezeptoren vermittelt wird. Die Blockierung von AMPA/Kainate Rezeptoren allein jedoch erhöhte überraschender Weise im Mittel die Antwort von MCs und reduzierte im Mittel die Antwort von INs. Die Blockierung von NMDA Rezeptoren allein lösten im Mittel geringe oder keine Veränderung der Antworten von MCs and INs aus. Außerdem hatten die Antagonisten für beide Glutamatrezeptoren unterschiedliche Einflüsse auf Größe und Zeitverlauf individueller MC- und IN- Antworten und veränderten daher das räumlich-zeitliche Aktivitätsmuster innerhalb der Nervenzellpopulation. Die Blockierung von GABA(A)-Rezeptoren erhöhte spontane und geruchsinduzierte Feuerraten in MCs und induzierten oft rhythmische, stoßweise Aktivität. Die Blockierung von GABA(A)- und AMPA/Kainate-Rezeptoren hob überdies geruchsinduzierte Oszillationen im Feldpotenzial auf. Die Blockierung von GABA(B)-Rezeptoren verringerte den Kalziumeinstrom in die Endigungen afferenter sensorischer Axone und modulierte den Zeitverlauf von MC-Antworten. Die Ergebnisse zeigen, dass (1) die Aktivität der Interneurone während der Geruchsantwort hauptsächlich von AMPA/Kainate-Rezeptoren abhängt, (2) die Interaktionen zwischen Mitralzellen und Interneuronen die Gesamtaktivität des Ausgangssingnales des olfaktorischen Bulbus regulieren, (3) AMPA/Kainate-Rezeptoren und GABA(A)-Rezeptoren der Synchronisation geruchsabhängiger Gruppen von Nervenzellen zugrunde liegen und (4) geruchsspezifische Muster im Ausgangssignal des olfaktorischen Bulbus durch Schaltkreise geformt werden, die iGlu Rezeptoren und GABA Rezeptoren enthalten. Diese Ergebnisse ermöglichen Einblick in die Mechanismen die der Verarbeitung geruchskodierender Aktivitätsmuster im olfaktorischen Bulbus unterliegen

Odor-Based Molecular Communications: State-of-the-Art, Vision, Challenges, and Frontier Directions

Humankind mimics the processes and strategies that nature has perfected and uses them as a model to address its problems. That has recently found a new direction, i.e., a novel communication technology called molecular communication (MC), using molecules to encode, transmit, and receive information. Despite extensive research, an innate MC method with plenty of natural instances, i.e., olfactory or odor communication, has not yet been studied with the tools of information and communication technologies (ICT). Existing studies focus on digitizing this sense and developing actuators without inspecting the principles of odor-based information coding and MC, which significantly limits its application potential. Hence, there is a need to focus cross-disciplinary research efforts to reveal the fundamentals of this unconventional communication modality from an ICT perspective. The ways of natural odor MC in nature need to be anatomized and engineered for end-to-end communication among humans and human-made things to enable several multi-sense augmented reality technologies reinforced with olfactory senses for novel applications and solutions in the Internet of Everything (IoE). This paper introduces the concept of odor-based molecular communication (OMC) and provides a comprehensive examination of olfactory systems. It explores odor communication in nature, including aspects of odor information, channels, reception, spatial perception, and cognitive functions. Additionally, a comprehensive comparison of various communication systems sets the foundation for further investigation. By highlighting the unique characteristics, advantages, and potential applications of OMC through this comparative analysis, the paper lays the groundwork for exploring the modeling of an end-to-end OMC channel, considering the design of OMC transmitters and receivers, and developing innovative OMC techniques

An Investigation on the Role of Spike Latency in an Artificial Olfactory System

Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time

Pharmacological analysis of ionotropic glutamate and GABA receptor function in neuronal circuits of the zebrafish olfactory bulb

In the olfactory bulb and other brain areas, basic cellular and synaptic properties of individual neurons have been studied extensively in reduced preparations. Nevertheless, it is still poorly understood how intactions between multiple neurons shape spatio-temporal activity patterns and give rise to the computational properties of the the intact circuit. In this thesis, I used pharmacological manipulations of excitatory and inhibitory neurotransmitter receptors to examine the synaptic interactions underlying spontaneous and odor-evoked activity patterns in the intact olfactory bulb of zebrafish. Electrophysiological and one- and two-photon calcium imaging methods were used to record activity from the principal neurons of the OB (mitral cells, MCs), their sensory input, and local interneurons. The combined blockade of AMPA/kainate and NMDA receptors abolished odor-evoked excitation of MCs, indicating that sensory input to the OB is mediated by ionotropic glutamate receptors. Surprisingly, however, the blockade of AMPA/Kainiate receptors alone increased the mean response of MCs and decreased the mean response of interneurons (INs), and the blockade of NMDA receptors caused little or no change in the mean responses of MCs and INs. In addition, antagonists of both glutamate receptor types had diverse effects on the magnitude and time course of individual MC and IN responses and, thus, changed spatio-temporal activity patterns across neuronal populations. The blockade of GABA(A) receptors increased spontaneous and odor evoked firing rates of mitral cells and often induced rhythmic bursting. Moreover, the blockade of, GABA(A) or AMPA/kainate receptors abolished fast oscillatory activity in the local field potential. Blockade of GABA(B) receptors reduced calcium influx in afferent sensory axons and modulated response time courses of mitral cells. These results indicate that (1) IN activity during an odor response depends mainly on AMPA/Kainiate receptor input, (2) interactions between MCs and INs regulate the total OB output activity, (3) AMPA/Kainiate receptors and GABA(A) receptors underly the synchronization of odor-dependent neuronal ensembles and (4) odor-specific patterns of OB output activity are shaped by circuits containing iGlu receptors and GABA receptors. These results provide insights into the mechanisms underlying the processing of odor-encoding activity patterns in the OB