Biomimetic set up for chemosensor-based machine olfaction

The thesis falls into the field of machine olfaction and accompanying experimental set up for chemical gas sensing. Perhaps more than any other sensory modality, chemical sensing faces with major technical and conceptual challenges: low specificity, slow response time, long term instability, power consumption, portability, coding capacity and robustness. There is an important trend of the last decade pushing artificial olfaction to mimic the biological olfaction system of insects and mammalians. The designers of machine olfaction devices take inspiration from the biological olfactory system, because animals effortlessly accomplish some of the unsolved problems in machine olfaction. In a remarkable example of an olfactory guided behavior, male moths navigate over large distances in order to locate calling females by detecting pheromone signals both rapidly and robustly. The biomimetic chemical sensing aims to identify the key blocks in the olfactory pathways at all levels from the olfactory receptors to the central nervous system, and simulate to some extent the operation of these blocks, that would allow to approach the sensing performance known in biological olfactory system of animals. New technical requirements arise to the hardware and software equipment used in such machine olfaction experiments. This work explores the bioinspired approach to machine olfaction in depth on the technological side. At the hardware level, the embedded computer is assembled, being the core part of the experimental set up. The embedded computer is interfaced with two main biomimetic modules designed by the collaborators: a large-scale sensor array for emulation of the population of the olfactory receptors, and a mobile robotic platform for autonomous experiments for guiding olfactory behaviour. At the software level, the software development kit is designed to host the neuromorphic models of the collaborators for processing the sensory inputs as in the olfactory pathway. Virtualization of the set up was one of the key engineering solutions in the development. Being a device, the set up is transformed to a virtual system for running data simulations, where the software environment is essentially the same, and the real sensors are replaced by the virtual sensors coming from especially designed data simulation tool. The proposed abstraction of the set up results in an ecosystem containing both the models of the olfactory system and the virtual array. This ecosystem can loaded from the developed system image on any personal computer. In addition to the engineering products released in the course of thesis, the scientific results have been published in three journal articles, two book chapters and conference proceedings. The main results on validation of the set up under the scenario of robotic odour localization are reported in the book chapters. The series of three journal articles covers the work on the data simulation tool for machine olfaction: the novel model of drift, the models to simulate the sensor array data based on the reference data set, and the parametrized simulated data and benchmarks proposed for the first time in machine olfaction. This thesis ends up with a solid foundation for the research in biomimetic simulations and algorithms on machine olfaction. The results achieved in the thesis are expected to give rise to new bioinspired applications in machine olfaction, which could have a significant impact in the biomedical engineering research area.Esta tesis se enmarca en el campo de bioingeneria, mas particularmente en la configuración de un sistema experimental de sensores de gases químicos. Quizás más que en cualquier otra modalidad de sensores, los sensores químicos representan un conjunto de retos técnicos y conceptuales ya que deben lidiar con problemas como su baja especificidad, su respuesta temporal lenta, su inestabilidad a largo plazo, su alto consumo enérgético, su portabilidad, así como la necesidad de un sistema de datos y código robusto. En la última década, se ha observado una clara tendencia por parte de los sistemas de machine olfaction hacia la imitación del sistema de olfato biológico de insectos y mamíferos. Los diseñadores de estos sistemas se inspiran del sistema olfativo biológico, ya que los animales cumplen, sin apenas esfuerzo, algunos de los escenarios no resueltos en machine olfaction. Por ejemplo, las polillas machos recorren largas distancias para localizar las polillas hembra, detectando sus feromonas de forma rápida y robusta. La detección biomimética de gases químicos tiene como objetivo identificar los elementos fundamentales de la vía olfativa a todos los niveles, desde los receptores olfativos hasta el sistema nervioso central, y simular, en cierta medida, el funcionamiento de estos bloques, lo que permitiría acercar el rendimiento de la detección al rendimiento de los sistemas olfativos conociodos de los animales. Esto conlleva nuevos requisitos técnicos a nivel de equipamiento tanto hardware como software utilizado en este tipo de experimentos de machine olfaction. Este trabajo propone un enfoque bioinspirado para la ¿machine olfaction¿, explorando a fondo la parte tecnológica. A nivel hardware, un ordenador embedido se ha ensamblado, siendo ésta la parte más importante de la configuración experimental. Este ordenador integrado está interconectado con dos módulos principales biomiméticos diseñados por los colaboradores: una matriz de sensores a gran escala y una plataforma móvil robotizada para experimentos autónomos. A nivel software, el kit de desarrollo software se ha diseñado para recoger los modelos neuromórficos de los colaboradores para el procesamiento de las entradas sensoriales como en la vía olfativa biológica. La virtualización del sistema fue una de las soluciones ingenieriles clave de su desarrollo. Al ser un dispositivo, el sistema se ha transformado en un sistema virtual para la realización de simulaciones de datos, donde el entorno de software es esencialmente el mismo, y donde los sensores reales se sustituyen por sensores virtuales procedentes de una herramienta de simulación de datos especialmente diseñada. La propuesta de abstracción del sistema resulta en un ecosistema que contiene tanto los modelos del sistema olfativo como la matriz virtual . Este ecosistema se puede cargar en cualquier ordenador personal como una imagen del sistema desarrollado. Además de los productos de ingeniería entregados en esta tesis, los resultados científicos se han publicado en tres artículos en revistas, dos capítulos de libros y los proceedings de dos conferencias internacionales. Los principales resultados en la validación del sistema en el escenario de la localización robótica de olores se presentan en los capítulos del libro. Los tres artículos de revistas abarcan el trabajo en la herramienta de simulación de datos para machine olfaction: el novedoso modelo de drift, los modelos para simular la matriz de sensores basado en el conjunto de datos de referencia, y la parametrización de los datos simulados y los benchmarks propuestos por primera vez en machine olfaction. Esta tesis ofrece una base sólida para la investigación en simulaciones biomiméticas y en algoritmos en machine olfaction. Los resultados obtenidos en la tesis pretenden dar lugar a nuevas aplicaciones bioinspiradas en machine olfaction, lo que podría tener un significativo impacto en el área de investigación en ingeniería biomédic

Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning

It is often assumed that insects are “primitive” animals, without the ability to exhibit complex learning behaviour. Fortunately, their tiny brains quite often surprise us with their performance. This thesis investigates the plasticity mechanisms of the insect brain through the research method of neurorobotics, i.e., the development of a physical agent, equipped with a silicon brain. In order to implement such a brain, we have chosen to model it directly onto hardware. Not only does this allow us to take advantage of the inherent hardware parallelism, but the robot can also behave in a completely autonomous mode, without having to communicate with the software simulator of a remote machine. FPGAs offer both the option for such a lowlevel design approach and the flexibility required in computational studies of biological neural networks. With the use of VHDL (a hardware description language), we develop a simulator for neural networks, designed as a series of computational modules, running in parallel and solving the differential equations which describe neural processes. It has the ability to simulate networks with spiking neurons that follow a phenomenological model, proposed by Izhikevich, which requires only 13 operations per 1 ms of simulation. The synaptic plasticity mechanism can be either that of spike timing-dependent plasticity (STDP) or a modified version of STDP which is also affected by neuromodulators. There are no constraints, as far as the connectivity pattern is concerned. The hardware simulator is then added as a peripheral to an embedded system so that it can be more easily controlled through software and connected to a robot. We show that this hardware system is able to model networks with hundreds of neurons and with a speed performance that is better than real-time. With some slight modifications, it could also scale up to thousands of neurons, starting to approach the size of the insect brain. Subsequently, we use the simulator in order to model a neural network with an architecture inspired by the insect brain, representing the connectivity of the antennal lobe, the mushroom body and the lateral horn, structures which are part of the insect’s olfactory pathway. Our silicon brain is then attached to a robot and its limits and capabilities are tested in a series of experiments. The experiments involve tasks of associative learning inside an arena which is based on a T-maze set-up usually employed in behavioural experiments with flies. The robot is trained to associate different stimuli (or combinations of stimuli) with a punishment, as indicated by the presence of a light source. We observe that the robot can solve most of the tasks, including elemental learning, discrimination learning, biconditional discrimination and negative patterning but fails to solve the problem of positive patterning. It is concluded that the architecture of the insect’s olfactory pathway has the computational efficiency to solve even non-elemental learning tasks. However, this pattern of results does not precisely match the fly, suggesting we have not fully understood the learning mechanisms involved. Moreover, embedding the learning circuit in robot behaviour reveals that the simple version of STDP is not the appropriate mechanism which can link neural plasticity to learning behaviour. Although the modified version of STDP is more suitable, it remains problematic as well as sensitive to timing issues. Therefore, we propose that STDP might function more as a “priming” process rather than as the basic learning mechanism

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

Ontogenesis of the Corpora Pedunculata: Integral Relay Structures of Chemosensory Content Addressable Memory Networks of Hexapods: A Synthesis of Development and Function

Biological memory is the temporal storage of information as a function of evolution. Several mechanisms have evolved by which memory can be stored. There are two components involved in the storage of memory in metazoan organisms. Innate memory is strictly teleonomically determined, and hence, depends on the phylogenic predisposition of an organisms' ontogenesis. 'Learned' memory is, in contrast, strictly ontogenically determined and, hence, influenced by the organisms environment. Whilst strictly ontogenic determined memory is stored in the spatial arrangement of nerve cells, phylogenic memory is stored in the sequential arrangement of the four components of the DNA. Accordingly, ontogenic memory is lost in subsequent generations, whereas phylogenic memory is passed on and recalled during the course of evolution. Insects are among the best understood organisms. The fruit fly Drosophila melanogaster, for instance, has been widely used as a model to unravel the genetic components of development. Most of the genes that are involved in this process are known. Other insect species have been physiologically and behaviourally well researched. By assembling the information derived from the latest research on Drosophila melanogaster and other insect species, I have made the attempt to characterise the different components of molecular memory formation (hereafter referred to as mnemogenesis) in insects. Chemosensory memory pathways of Drosophila are composed of at least two different entities: the morphogenic fields such as the peripheral and the central nervous system. I have concluded that during the ontogenesis of the Drosophila chemosensory memory pathways, genes are active that function as modules during this process. Most of the genes which mediate this process are not strictly employed during the morphogenesis of the chemosensory memory pathways. However, they are redeployed to a large extent during development of other germ layers and morphogenic fields, as well. Only certain key genes, which expression is initiated by the several coinciding morphogenic signals, determine the specificity of the different components of the chemosensory memory pathways. Hence, the specificity of the chemosensory memory pathways of Drosophila is determined by the temporally and spatially distinct expression of genes, in addition to the modification of their products. Whilst stage and cell specific gene expression is primarily regulated on the level of chromosome structure and transcriptional activity, the specific function of genes that are expressed in the different regions during different stages of ontogenesis is generated by messenger ribonucleic acid and protein processing. The morphogenic cascades are probably frozen down once the chemosensory memory pathways have reached the state of maturity. The mature insect has maintained the ability to employ some components of the developmental cascade to modulate its memory in response to environmental stimuli. Imaginal chemosensory memory pathways comprise at least four levels. Chemosensory receptor (level I) cells receive environmental information. Projection neurones (level II) reduce the background noise and transfer the information to diverging memory structures, in addition to the control centres (levels III/i and III/ii). Whereas memory structures modulate chemosensory information, the control centres feed this modulated information into output fibres that link the chemosensory memory networks with the premotor fibres (level IV). The function of the memory structures, which in insects are called the corpora pedunculata, is to compare input information to the information stored intrinsically in these organs. The information that is stored intrinsic to these structures is able to modulate the behaviour of an signal, which exits the chemosensory pathways via the premotor neurones. It has been postulated that the modulation of this information depends on the synaptic configuration within the corpora pedunculata. Hence, the synaptic arrangement is thought to underlie the modulation of the information transfer within the chemosensory memory networks. Long term memory is associated with the alteration of this synaptic configuration, which in turn requires the activity of several genetic circuits. Intriguingly, these genetic circuits are probably identical to those employed during axonogenesis, in addition to other morphogenic events

From Insect Pheromones to Mating Disruption

The present book, a reprint of the successful Insects Special Issue "From Insect Pheromones to Mating Disruption: Theory and Practice", includes laboratory and field studies dealing with insect pheromones, as well as on mating disruption efficacy against insect species of economic importance, with special reference to the development and optimization of mating disruption approaches, their mechanisms of action, and possible non-target effects

Development and Application of pH-sensitive Fluorescent Probes to Study Synaptic Activity in the Brain

This thesis describes efforts at the interface of chemistry and neuroscience to design and characterize fluorescent probes capable of tracing neurotransmitters from individual release sites in brain tissue. As part of the Fluorescent False Neurotransmitters (FFNs) program, small organic fluorophores have been developed that undergo uptake into specific presynaptic release sites and synaptic vesicles by utilizing the native protein machinery, which can then be released during neuronal firing. The most advanced generation of FFNs are pH-sensitive, and display an increase in fluorescence when released from the acidic vesicular lumen into the extracellular space, called a “FFN Flash.” In Chapter 2, the utility of the dopamine-selective and pH-sensitive functionality of FFN102 to study the mechanisms that regulate changes in pre-synaptic plasticity, a critical component of neurotransmission was explored. This included using the FFN flash to quantitatively trace dopamine release, changes in the release probability of individual release sites, and changes in vesicular loading that can affect quantal size. The second goal of this thesis research, as detailed in Chapters 3 and 4, sought to expand the substrate scope of the FFN program to neurotransmitter systems other than dopamine. Described in Chapter 3, is the identification of a fluorescent phenylpyridinium, APP+, with excellent labeling for dopamine, norepinephrine, and serotonin neurons, however, the properties of the probe were found to be ill-suited for measuring neurotransmitter release. As a result, it was concluded that this class of compounds was not suitable for generating viable FFN leads. In contrast, Chapter 4 highlights the design, synthesis, and screening towards generating the novel noradrenergic-specific FFN, FFN270. This probe was further tested for application in acute murine brain slices where it labeled noradrenergic neurons, and was demonstrated to release upon stimulation. This chapter also describes the application of this compound in a series of in vivo experiments, where the ability to measure norepinephrine release from individual release sites was demonstrated in a living animal for the first time. This work opens the possibility for many exciting future FFN experiments studying the presynaptic regulation of neurotransmission in vivo

Synthesis of new pyrazolium based tunable aryl alkyl ionic liquids and their use in removal of methylene blue from aqueous solution

In this study, two new pyrazolium based tunable aryl alkyl ionic liquids, 2-ethyl-1-(4-methylphenyl)-3,5- dimethylpyrazolium tetrafluoroborate (3a) and 1-(4-methylphenyl)-2-pentyl-3,5-dimethylpyrazolium tetrafluoroborate (3b), were synthesized via three-step reaction and characterized. The removal of methylene blue (MB) from aqueous solution has been investigated using the synthesized salts as an extractant and methylene chloride as a solvent. The obtained results show that MB was extracted from aqueous solution with high extraction efficiency up to 87 % at room temperature at the natural pH of MB solution. The influence of the alkyl chain length on the properties of the salts and their extraction efficiency of MB was investigated