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

Real-time classification of multivariate olfaction data using spiking neural networks

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an SNN-based solution for the classification of rank-order spiking patterns to provide continuous recognition results in real-time. The SNN classifier is deployed on a neuromorphic hardware system that enables massively parallel and low-power processing on incoming rank-order patterns. Offline learning is used to store the reference rank-order patterns, and an inbuilt nearest neighbor classification logic is applied by the neurons to provide recognition results. The proposed system was evaluated using two different datasets including rank-order spiking data from previously established olfactory systems. The continuous classification that was achieved required a maximum of 12.82% of the total pattern frame to provide 96.5% accuracy in identifying corresponding target gases. Recognition results were obtained at a nominal processing latency of 16ms for each incoming spike. In addition to the clear advantages in terms of real-time operation and robustness to inconsistent rank-orders, the SNN classifier can also detect anomalies in rank-order patterns arising due to drift in sensing arrays

INTEGRATION OF CMOS TECHNOLOGY INTO LAB-ON-CHIP SYSTEMS APPLIED TO THE DEVELOPMENT OF A BIOELECTRONIC NOSE

This work addresses the development of a lab-on-a-chip (LOC) system for olfactory sensing. The method of sensing employed is cell-based, utilizing living cells to sense stimuli that are otherwise not easily sensed using conventional transduction techniques. Cells have evolved over millions of years to be exquisitely sensitive to their environment, with certain types of cells producing electrical signals in response to stimuli. The core device that is introduced here is comprised of living olfactory sensory neurons (OSNs) on top of a complementary metal-oxide-semiconductor (CMOS) integrated circuit (IC). This hybrid bioelectronic approach to sensing leverages the sensitivity of OSNs with the electronic signal processing capability of modern ICs. Intimately combining electronics with biology presents a number of unique challenges to integration that arise from the disparate requirements of the two separate domains. Fundamentally the obstacles arise from the facts that electronic devices are designed to work in dry environments while biology requires not only a wet environment, but also one that is precisely controlled and non-toxic. Design and modeling of such heterogeneously integrated systems is complicated by the lack of tools that can address the multiple domains and techniques required for integration, namely IC design, fluidics, packaging, and microfabrication, and cell culture. There also arises the issue of how to handle the vast amount of data that can be generated by such systems, and specifically how to efficiently identify signals of interest and communicate them off-chip. The primary contributions of this work are the development of a new packaging scheme for integration of CMOS ICs into fluidic LOC systems, a methodology for cross-coupled multi-domain iterative modeling of heterogeneously integrated systems, demonstration of a proof-of-concept bioelectronic olfactory sensor, and a novel event-based technique to minimize the bandwidth required to communicate the information contained in bio-potential signals produced by dense arrays of electrically active cells

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Nano-Bio Hybrid Electronic Sensors for Chemical Detection and Disease Diagnostics

The need to detect low concentrations of chemical or biological targets is ubiquitous in environmental monitoring and biomedical applications. The goal of this work was to address challenges in this arena by combining nanomaterials grown via scalable techniques with chemical receptors optimized for the detection problem at hand. Advances were made in the CVD growth of graphene, carbon nanotubes and molybdenum disulfide. Field effect transistors using these materials as the channel were fabricated using methods designed to avoid contamination of the nanomaterial surfaces. These devices were used to read out electronic signatures of binding events of molecular targets in both vapor and solution phases. Single-stranded DNA functionalized graphene and carbon nanotubes were shown to be versatile receptors for a wide variety of volatile molecular targets, with characteristic responses that depended on the DNA sequence and the identity of the target molecule, observable down to part-per-billion concentrations. This technology was applied to increasingly difficult detection challenges, culminating in a study of blood plasma samples from patients with ovarian cancer. By working with large arrays of devices and studying the devices\u27 responses to pooled plasma samples and plasma samples from 24 individuals, sufficient data was collected to identify statistically robust patterns that allow samples to be classified as coming from individuals who are healthy or have either benign or malignant ovarian tumors. Solution-phase detection experiments focused on the design of surface linkers and specific receptors for medically relevant molecular targets. A non-covalent linker was used to attach a known glucose receptor to carbon nanotubes and the resulting hybrid was shown to be sensitive to glucose at the low concentrations found in saliva, opening up a potential pathway to glucose monitoring without the need for drawing blood. In separate experiments, molybdenum disulfide transistors were functionalized with a re-engineered variant of a μ-opiod receptor, a cell membrane protein that binds opiods and regulates pain and reward signaling in the body. The resulting devices were shown to bind opiods with affinities that agree with measurements in the native state. This result could enable not only an advanced opiod sensor but moreover could be generalized into a solid-state drug testing platform, allowing the interactions of novel pharmaceuticals and their target proteins to be read out electronically. Such a system could have high throughput due to the quick measurement, scalable device fabrication and high sensitivity of the molybdenum disulfide transistor

Field-Effect Sensors

This Special Issue focuses on fundamental and applied research on different types of field-effect chemical sensors and biosensors. The topics include device concepts for field-effect sensors, their modeling, and theory as well as fabrication strategies. Field-effect sensors for biomedical analysis, food control, environmental monitoring, and the recording of neuronal and cell-based signals are discussed, among other factors

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Microbial and non-microbial volatile fingerprints : potential clinical applications of electronic nose for early diagnoses and detection of diseases

This is the first study to explore the potential applications of using qualitative volatile fingerprints (electronic nose) for early detection and diagnosis of diseases such as dermatophytosis, ventilator associated pneumonia and upper gastrointestinal cancer. The investigations included in vitro analysis of various dermatophyte species and strains, antifungal screening, bacterial cultures and associated clinical specimens and oesophageal cell lines. Mass spectrometric analyses were attempted to identify possible markers. The studies that involved e-nose comparisons indicated that the conducting polymer system was unable to differentiate between any of the treatments over the experimental period (120 hours). Metal oxide-based sensor arrays were better suited and differentiated between four dermatophyte species within 96 hours of growth using principal component analysis and cluster analysis (Euclidean distance and Ward’s linkage) based on their volatile profile patterns. Studies on the sensitivity of detection showed that for Trichophyton mentagrophytes and T. rubrum it was possible to differentiate between log3, log5 and log7 inoculum levels within 96 hours. The probabilistic neural network model had a high prediction accuracy of 88 to 96% depending on the number of sensors used. Temporal volatile production patterns studied at a species level for a Microsporum species, two Trichophyton species and at a strain level for the two Trichophyton species; showed possible discrimination between the species from controls after 120 hours. The predictive neural network model misclassified only one sample. Data analysis also indicated probable differentiation between the strains of T. rubrum while strains of T. mentagrophytes clustered together showing good similarity between them. Antifungal treatments with itraconazole on T. mentagrophytes and T. rubrum showed that the e-nose could differentiate between untreated fungal species from the treated fungal species at both temperatures (25 and 30°C). However, the different antifungal concentrations of 50% fungal inhibition and 2 ppm could not be separated from each other or the controls based on their volatiles. Headspace analysis of bacterial cultures in vitro indicated that the e-nose could differentiate between the microbial species and controls in 83% of samples (n=98) based on a four group model (gram-positive, gram-negative, fungi and no growth). Volatile fingerprint analysis of the bronchoalveolar lavage fluid accurately separated growth and no growth in 81% of samples (n=52); however only 63% classification accuracy was achieved with a four group model. 12/31 samples were classified as infected by the e-nose but had no microbiological growth, further analysis suggested that the traditional clinical pulmonary infection score (CPIS) system correlated with the e-nose prediction of infection in 68% of samples (n=31). No clear distinction was observed between various human cell lines (oesophageal and colorectal) based on volatile fingerprints within one to four hours of incubation, although they were clearly separate from the blank media. However, after 24 hours one of the cell lines could be clearly differentiated from the others and the controls. The different gastrointestinal pathologies (forming the clinical samples) did not show any specific pattern and thus could not be distinguished. Mass spectrometric analysis did not detect distinct markers within the fungal and cell line samples, but potential identifiers in the fungal species such as 3-Octanone, 1-Octen-3-ol and methoxybenzene including high concentration of ammonia, the latter mostly in T. mentagrophytes, followed by T. rubrum and Microsporum canis, were found. These detailed studies suggest that the approach of qualitative volatile fingerprinting shows promise for use in clinical settings, enabling rapid detection/diagnoses of diseases thus eventually reducing the time to treatment significantly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo