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Over the last decade advances in multineuron simultaneous recording techniques have produced huge amounts of data. This has led to the investigation of probable temporal relationships between spike times of neurons as manifestations of the underlying network structure. But the huge dimensionality of data makes the search for patterns difficult. Although this difficulty may be surpassed by employing massive computing resources, understanding the significance and relation of these temporal patterns to the underlying network structure and the causative activity is still difficult. To find such relationships in networks of excitatory neurons, a simplified network structure of feedforward chains called "Synfire chains" has been frequently employed. But in a recurrently connected network where activity from feedback connections is comparable to the feedforward chain, the basic assumptions underlying synfire chains are violated. In the first part of this thesis we propose the first-spike-latency based analysis as a low complexity method of studying the temporal relationships between neurons. Firstly, spike latencies being temporal delays measured at a particular epoch of time (onset of activity after a quiescent period) are a small subset of all the temporal information available in spike trains, thereby hugely reducing the amount of data that needs to be analyzed. We also define for the first time, "Synconset waves and chains" as a sequence of first-spike-times and the causative neuron chain. Using simulations, we show the efficacy of the synconset paradigm in unraveling feedforward chains of excitatory neurons even in a recurrent network. We further create a framework for going back and forth between network structure and the observed first-spike-latency patterns. To quantify these associations between network structure and dynamics we propose a likelihood measure based on Bayesian reasoning. This quantification is agnostic to the methods of association used and as such can be used with any of the existing approaches. We also show the benefits of such an analysis when the recorded data is subsampled, as is the case with most physiological recordings. In the subsequent part of our thesis we show two sample applications of first-spike-latency analysis on data acquired from multielectrode arrays. Our first application dwells on the intricacies of extracting first-spike-latency patterns from multineuron recordings using recordings of glutamate injured cultures. We study the significance of these patterns extracted vis-a-vis patterns that may be obtained from exponential spike latency distributions and show the differences between patterns obtained in injured and control cultures. In a subsequent application, we study the evolution of latency patterns over several days during the lifetime of a dissociated hippocampal culture.

Instrumental disparities and soil moisture are two of the key limitations in implementing spectroscopic techniques in the field. This study sought to address these challenges through two objectives. The first objective was to assess Visible-near infrared (VisNIR) and mid-infrared (MIR) spectroscopic approaches and explore the feasibility of transferring calibration models between laboratory and portable spectrometers. The second objective addressed the challenge of soil moisture and its impact on spectra. The portable spectrometers demonstrated comparable performance to their laboratory-based counterparts in both regions. Spiking with extra-weight, was the most effective calibration transfer method eliminating disparities between instruments. The samples were rewetted under nine controlled conditions for the moisture study. Results showed that spiking with extra weights significantly outperformed other techniques and model enhancement was insensitive to the moisture contents. Findings of this study will be helpful for development and deployment of in situ sensors to enable field implementation of spectroscopy.

Driving is one of the most common and preferred forms of transport used in our actual society. However, according to studies, it is also one of the most dangerous. One solution to increase safety on the road is applying technology to automate and prevent avoidable human errors. Nevertheless, despite the efforts to obtain reliable systems, we have yet to find a reliable and safe enough solution for solving autonomous driving. One of the reasons is that many drives are done in conditions far from the ideal, with variable lighting conditions and fast-paced, unpredictable environments. This project develops and evaluates an algorithm that takes the input of dynamic vision sensors (DVS) and runs on neuromorphic spiking neural networks (SNN) to obtain a robust road lane tracking system. We present quantitative and qualitative metrics that evaluate the performance of lane recognition in low light conditions against conventional algorithms. This project is motivated by the main advantages of neuromorphic vision sensors: recognizing a high dynamic range and allowing a high-speed image capture. Another improvement of this system is the computational speed and power efficiency that characterize neuromorphic hardware based on spiking neural networks. The results obtained show a similar accuracy of this new algorithm compared to previous implementations on conventional hardware platforms. Most importantly, it accomplishes the proposed task with lower latency and computing power requirements than previous algorithms. / Att köra bil är ett av de vanligaste och mest populära transportsätten i vårt samhälle. Enligt forskningen är det också ett av de farligaste. En lösning för att öka säkerheten på vägarna är att med teknikens hjälp automatisera bilkörningen och på så sätt förebygga misstag som beror på den mänskliga faktorn. Trots ansträngningarna för att få fram tillförlitliga system har man dock ännu inte hittat en tillräckligt tillförlitlig och säker lösning för självkörande bilar. En av orsakerna till det är att många körningar sker under förhållanden som är långt ifrån idealiska, med varierande ljusförhållanden och oförutsägbara miljöer i höga hastigheter. I det här projektet utvecklar och utvärderar vi en algoritm som tar emot indata från dynamiska synsensorer (Dynamic Vision Sensors, DVS) och kör datan på neuromorfiska pulserande neuronnät (Spiking Neural Networks, SNN) för att skapa ett robust system för att läsa av vägbanan. Vi presenterar en kvantitativ och kvalitativ utvärdering av hur väl systemet läser av körbanans linjer i svagt ljus, och jämför därefter resultaten med dem för tidigare algoritmer. Detta projekt motiveras av de viktigaste fördelarna med neuromorfiska synsensorer: brett dynamiskt omfång och hög bildtagningshastighet. En annan fördel hos detta system är den korta beräkningstiden och den energieffektivitet som kännetecknar neuromorfisk hårdvara baserad på pulserande neuronnät. De resultat som erhållits visar att den nya algoritmen har en liknande noggrannhet som tidigare algoritmer på traditionella hårdvaruplattformar. I jämförelse med den traditionella tekniken, utför algoritmen i den föreliggande studien sin uppgift med kortare latenstid och lägre krav på processorkraft. / La conducción es una de las formas de transporte más comunes y preferidas en la actualidad. Sin embargo, diferentes estudios muestran que también es una de las más peligrosas. Una solución para aumentar la seguridad en la carretera es aplicar la tecnología para automatizar y prevenir los evitables errores humanos. No obstante, a pesar de los esfuerzos por conseguir sistemas fiables, todavía no hemos encontrado una solución suficientemente fiable y segura para resolver este reto. Una de las razones es el entorno de la conducción, en situaciones que distan mucho de las ideales, con condiciones de iluminación variables y entornos rápidos e imprevisibles. Este proyecto desarrolla y evalúa un algoritmo que toma la entrada de sensores de visión dinámicos (DVS) y ejecuta su computación en redes neuronales neuromórficas (SNN) para obtener un sistema robusto de seguimiento de carriles en carretera. Presentamos métricas cuantitativas y cualitativas que evalúan el rendimiento del reconocimiento de carriles en condiciones de poca luz, frente a algoritmos convencionales. Este proyecto está motivado por la validación de las ventajas de los sensores de visión neuromórficos: el reconocimiento de un alto rango dinámico y la captura de imágenes de alta velocidad. Otra de las mejoras que se espera de este sistema es la velocidad de procesamiento y la eficiencia energética que caracterizan al hardware neuromórfico basado en redes neuronales de impulsos. Los resultados obtenidos muestran una precisión similar entre el nuevo algoritmo en comparación con implementaciones anteriores en plataformas convencionales. Y lo que es más importante, realiza la tarea propuesta con menor latencia y requisitos de potencia de cálculo.