A 4×4 Logarithmic Spike Timing Encoding Scheme for Olfactory Sensor Applications

International audienceThis paper presents a 4×4 logarithmic spike-timing encoding scheme used to translate the output of an integrated tin oxide gas sensor array into spike sequence, which is exploited to perform gas recognition. Hydrogen, Ethanol and Carbon monoxide were used to characterize the gas sensor array. The collected data were then used to test the proposed circuit for spike encoding and gas recognition. Simulation results illustrate that a particular analyte gas generates a unique spike pattern with certain spike ordering sequence, which is independent of the gas concentration. This unique spike sequence can thus be used to recognize different gases. In addition, the concentration information can also be extracted from the time-to-the-first spike in the sequence making it possible to perform not only gas/odor recognition but quantification as well

A review of current neuromorphic approaches for vision, auditory, and olfactory sensors

Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

Stereo-olfaction with a sniffing neuromorphic robot using spiking neurons

Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a neuromorphic robot using stereo-olfaction and a sniffing system based on nonselective chemosensors that mimic the animal behavior of tracking a specific odor. In order to be able to go toward an odor source, two tasks must be performed : 1) estimation of the gas-concentration gradient and 2) gas recognition independent of the intensity. It is shown how these two tasks can be implemented with artificial spiking neurons in a biologically inspired approach

Basic coding activities of populations of Xenopus laevis olfactory receptor neurons recorded with a fast confocal line illumination microscope

Das Geruchssystem ist in der Lage, mittels sogenannter kombinatorischer Kodierung einen hochdimensionalen Geruchsraum durch eine begrenzte Anzahl von olfaktorischen Rezeptorneuronen (ORN) abzutasten. Hierbei weisen verschiedene ORN-Klassen eine breite und gleichzeitig spezifische Geruchssensitivität auf, durch welche ein geruchsspezifisches Antwortmuster auf Populationen von Mitral-/Tufted Zellen (M/T) des bulbus olfactoris (OB) abgebildet wird. Neueren Untersuchungen zufolge sind diese Antwortmuster nicht notwendigerweise statisch, sondern enthalten Information in ihrer zeitlichen Entwicklung. Im OB von Larven des Krallenfrosches Xenopus laevis wurde herausgefunden, dass sowohl Geruchsidentität als auch -Konzentration besser vorhergesagt wird durch M/T Antwortlatenzmuster als durch durchschnittliche Feuerraten. Diese Arbeit befasst sich mit der Messung von ORN-Aktivität auf verschiedenen raumzeitlichen Skalen. Auf der Ebene von ORN Populationen wurde mit Hilfe von konfokaler Mikroskopie und [Ca2+] -sensitiven Fluoreszenzfarbstoffen untersucht, in wie weit Latenzmuster auftreten. Es wurde gezeigt, dass Latenzmuster im Unterschied zu M/T Zellen eine geringere Vorhersagekraft für die Geruchsstoffkonzentration besitzen als Feuerratenmuster. Außerdem wiesen Ensemble-Feuerraten einen größeren dynamischen Bereich bezüglich der Geruchsstoffkonzentration auf als Latenzen. Durch eine Kombination von schneller (1,25 kHz) [Ca2+] -Bildgebung und whole-cell Patch-Clamp Technik in einzelnen ORNs wurde die zeitliche Entwicklung der dreidimensionalen intrazellulären Ca2+ -Konzentration während eines Depolarisationspulses gemessen. Mit Hilfe von pixelweiser Angleichung eines numerischen Modells wurden Ballungen spannungsabhängiger Ca2+ Kanäle (VGCC) auf der Oberfläche von ORN-Somata lokalisiert. Da der durchschnittliche gemessene VGCC-Kalziumioneneinstrom einen geringen Beitrag im Vergleich zum Ca2+ Generatorstrom darstellt (<80 pA bzw. geschätzt 900 pA), erklärte sich, warum einzelne Aktionspotentiale nicht mittels [Ca2+] Bildgebung gemessen werden konnten. Bezüglich VGCC-Häufung und möglicher Kolokalisation mit Kaliumkanälen hoher Leitfähigkeit (BK) wurde der Effekt von BK Blocker Iberiotoxin auf ORN-Reizantworten untersucht. In einer Untergruppe aller ORNs wurde eine Verringerung der Antwortamplituden nach Anwendung von Iberiotoxin festgestellt. Aus den gezeigten Ergebnissen wurde geschlossen, dass eine wichtige Funktion von Glomeruli im OB die Konversion von Geruchsinformation zwischen Feuerratenkodierung und Latenzkodierung sein müsse

A NEUROMORPHIC APPROACH TO TACTILE PERCEPTION

Ph.DDOCTOR OF PHILOSOPH

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

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