Energy Efficient Computing with Time-Based Digital Circuits

University of Minnesota Ph.D. dissertation. May 2019. Major: Electrical Engineering. Advisor: Chris Kim. 1 computer file (PDF); xv, 150 pages.Advancements in semiconductor technology have given the world economical, abundant, and reliable computing resources which have enabled countless breakthroughs in science, medicine, and agriculture which have improved the lives of many. Due to physics, the rate of these advancements is slowing, while the demand for the increasing computing horsepower ever grows. Novel computer architectures that leverage the foundation of conventional systems must become mainstream to continue providing the improved hardware required by engineers, scientists, and governments to innovate. This thesis provides a path forward by introducing multiple time-based computing architectures for a diverse range of applications. Simply put, time-based computing encodes the output of the computation in the time it takes to generate the result. Conventional systems encode this information in voltages across multiple signals; the performance of these systems is tightly coupled to improvements in semiconductor technology. Time-based computing elegantly uses the simplest of components from conventional systems to efficiently compute complex results. Two time-based neuromorphic computing platforms, based on a ring oscillator and a digital delay line, are described. An analog-to-digital converter is designed in the time domain using a beat frequency circuit which is used to record brain activity. A novel path planning architecture, with designs for 2D and 3D routes, is implemented in the time domain. Finally, a machine learning application using time domain inputs enables improved performance of heart rate prediction, biometric identification, and introduces a new method for using machine learning to predict temporal signal sequences. As these innovative architectures are presented, it will become clear the way forward will be increasingly enabled with time-based designs

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included

Micro-, Meso- and Macro-Dynamics of the Brain

Neurosciences, Neurology, Psychiatr

Towards The Development of Biosensors for the Detection of Microbiologically Influenced Corrosion (MIC)

Corrosion is one of the biggest concerns for mechanical integrity of infrastructure and infrastructural components, such as oil refineries, bridges and roads. The economic cost of corrosion is typically estimated to be between 1 to 5 % of the gross national product (GNP) of countries, of which the contribution of microbiologically influenced corrosion (MIC) is estimated to be between 10% and 50%. Current state-of-the-art approaches for detecting MIC primarily rely on ex-situ tests, including bacterial test kits (bug bottles); corrosion coupons, pigging deposits analysis and destructive analysis of MIC affected sites using SEM, TEM, and XRD. These ex-situ measurements do not capture the complexities and time sensitivities underlying MIC. This is owed to the fact that the proliferation of the microbial contamination is a dynamic and rapid process, and any delay can prove expensive as it is estimated that once the biofilm formation takes place the amount of biocides needed is magnitude of orders more as compared to when the bacteria are in planktonic form. Additionally, the field environment is a complex biotic and abiotic environment which is often difficult to replicate even in high fidelity laboratory models. Hence a real-time/pseudo real-time method of detection would greatly help reduce the costs and optimize biocide-based mitigation of MIC. To overcome the above-mentioned shortcomings associated with the state-of-the-art; this work is aimed at the development of a sensor substrate whereby highly specific detection can be carried out in the environment where the corrosion exists, in a real-time/pseudo real-time basis. More specifically, the research is aimed at the development of sensors based on a nanowire matrix functionalized with biomolecules which can perform this specific and real-time detection of MIC in the pipeline environment. Here, the detection of MIC is based on the binding of specific biomolecules causing MIC to organic molecules anchored on top of the nanowires. These sensors also need to be inexpensive (made of low-cost, earth abundant materials), have low power consumption, and robustly deployable. The primary component of the detection platforms are copper oxide nanowire arrays (CuONWs with lengths of 25 to 30 m, 50 to 100 nm in diameter) and silicon nanowires arrays (SiNWs with lengths of 5 to 8 m, 45 to 100 nm in diameter). They are synthesized using facile and scalable techniques and are selected for their robust electrical and mechanical properties. Electrochemical degradation studies of the NWs were performed in 3.5 wt. % NaCl solution and simulated produced water using polarization and electrochemical impedance spectroscopy (EIS). The NWs systems showed robust resistance to degradation despite higher surface area (as compared to bulk counterparts), and both diffusion limitations and charge transfer resistance was observed on the analysis of the impedance response. The ability to immobilize a variety of moieties on the nanowire platforms gives them the ability to detecting a wide variety of MIC biomarkers. The Biotin-Streptavidin (SA) complex was used as a proof of concept to test the viability of the NW arrays as a substrate for sensing. A custom test bed was built for the functionalized NW thin films, and cyclic voltammetry studies revealed a stable current response with time for 10nM and 10,000 nM SA concentrations. The use of different probes such as aptamers to larger immunoglobulin probes provides the flexibility to detect the full spectrum of biomarkers. The development of these next generation sensor platforms along with the methodologies employed to stabilize them and assemble them into functional devices are explored in detail in this dissertation

Identification of Dendritic Processing in Spiking Neural Circuits

A large body of experimental evidence points to sophisticated signal processing taking place at the level of dendritic trees and dendritic branches of neurons. This evidence suggests that, in addition to inferring the connectivity between neurons, identifying analog dendritic processing in individual cells is fundamentally important to understanding the underlying principles of neural computation. In this thesis, we develop a novel theoretical framework for the identification of dendritic processing directly from spike times produced by spiking neurons. The problem setting of spiking neurons is necessary since such neurons make up the majority of electrically excitable cells in most nervous systems and it is often hard or even impossible to directly monitor the activity within dendrites. Thus, action potentials produced by neurons often constitute the only causal and observable correlate of dendritic processing. In order to remain true to the underlying biophysics of electrically excitable cells, we employ well-established mechanistic models of action potential generation to describe the nonlinear mapping of the aggregate current produced by the tree into an asynchronous sequence of spikes. Specific models of spike generation considered include conductance-based models such as Hodgkin-Huxley, Morris-Lecar, Fitzhugh-Nagumo, as well as simpler models of the integrate-and-fire and threshold-and-fire type. The aggregate time-varying current driving the spike generator is taken to be produced by a dendritic stimulus processor, which is a nonlinear dynamical system capable of describing arbitrary linear and nonlinear transformations performed on one or more input stimuli. In the case of multiple stimuli, it can also describe the cross-coupling, or interaction, between various stimulus features. The behavior of the dendritic stimulus processor is fully captured by one or more kernels, which provide a characterization of the signal processing that is consistent with the broader cable theory description of dendritic trees. We prove that the neural identification problem, stated in terms of identifying the kernels of the dendritic stimulus processor, is mathematically dual to the neural population encoding problem. Specifically, we show that the collection of spikes produced by a single neuron in multiple experimental trials can be treated as a single multidimensional spike train of a population of neurons encoding the parameters of the dendritic stimulus processor. Using the theory of sampling in reproducing kernel Hilbert spaces, we then derive precise results demonstrating that, during any experiment, the entire neural circuit is projected onto the space of input stimuli and parameters of this projection are faithfully encoded in the spike train. Spike times are shown to correspond to generalized samples, or measurements, of this projection in a system of coordinates that is not fixed but is both neuron- and stimulus-dependent. We examine the theoretical conditions under which it may be possible to reconstruct the dendritic stimulus processor from these samples and derive corresponding experimental conditions for the minimum number of spikes and stimuli that need to be used. We also provide explicit algorithms for reconstructing the kernel projection and demonstrate that, under natural conditions, this projection converges to the true kernel. The developed methodology is quite general and can be applied to a number of neural circuits. In particular, the methods discussed span all sensory modalities, including vision, audition and olfaction, in which external stimuli are typically continuous functions of time and space. The results can also be applied to circuits in higher brain centers that receive multi-dimensional spike trains as input stimuli instead of continuous signals. In addition, the modularity of the approach allows one to extend it to mixed-signal circuits processing both continuous and spiking stimuli, to circuits with extensive lateral connections and feedback, as well as to multisensory circuits concurrently processing multiple stimuli of different dimensions, such as audio and video. Another important extension of the approach can be used to estimate the phase response curves of a neuron. All of the theoretical results are accompanied by detailed examples demonstrating the performance of the proposed identification algorithms. We employ both synthetic and naturalistic stimuli such as natural video and audio to highlight the power of the approach. Finally, we consider the implication of our work on problems pertaining to neural encoding and decoding and discuss promising directions for future research

Single Cell Analysis

Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits

Influence of sensorimotor µ rhythm phase and power on motor cortex excitability and plasticity induction, assessed with EEG-triggered TMS

In dieser Arbeit werden zwei Experimente vorgestellt, bei denen EEG-getriggerte transkranielle Magnetstimulation (TMS) an gesunden Probanden eingesetzt wurde, um die Rolle des sensomotorischen 8-14Hz µ-Rhythmus auf die kortikospinale Erregbarkeit (CSE) und die Induktion positiver Plastizität zu untersuchen. Unser Ziel war es, für Plastizitätsinduktion günstige Zeitpunkte im EEG zu identifizieren, um in Zukunft die Effektivität solcher zurzeit oft noch unzuverlässigen Anwendungen zu steigern. Unser EEG-TMS System interpretierte Oszillationen im EEG in Echtzeit und löste einen Stimulus aus, wenn bestimmte, vorher festgelegte Eigenschaften zutrafen. Die ‘Gehirnwellen’ im EEG entstehen durch synchronisierte Fluktuationen des Membranpotentials kortikaler Neurone, welche aufgrund ihrer intrakortikalen Kommunikationsfunktion wertvolle Informationen über neuronale Erregbarkeit vermitteln. Im Gegensatz zu “open-loop” TMS ermöglicht EEG-TMS nicht nur eine präzisere Erforschung der Funktion von Gehirnwellen, sondern auch die Umsetzung der gewonnenen Erkenntnisse in effizientere therapeutische Anwendungen. Speziell Oszillationen im Alpha-Frequenzbereich (8-14Hz) spielen eine bedeutsame Rolle, indem sie den Informationsfluss im Gehirn durch Hemmung aktuell irrelevanter Areale steuern, und zwar laut einer führenden Theorie als “asymmetrisch gepulste Inhibition” mit einem Maximum der Hemmung während der Hochpunkte (“Peaks”) und während hoher “Power” (∼ Amplitude). Der “µ-Rhythmus”, Wellen in alpha-Frequenz über dem sensomotorischen Kortex, scheint für diese Areale eine analoge Rolle wie das okzipitale Alpha für den visuellen Kortex zu spielen. Die CSE lässt sich durch die Amplitude der ausgelösten kontralateralen Muskelzuckungen (MEPs im EMG) quantifizieren. Im Vorexperiment erforschten wir den Einfluss der Power der µ-Wellen auf die CSE. 16 Teilnehmer wurden in einer Sitzung mit Einzelpuls-TMS des linken M1 stimuliert. Die Pulse wurden durch die momentane Power ausgelöst, 10 Dezile des individuellen µ-Powerspektrums wurden in pseudorandomisierter Reihenfolge angesteuert, verteilt auf 4 Stimulationsblöcke. Nach Berücksichtigung der “Inter-Trial-Intervalle” (ITIs, bekannter “Confounder”) und Normalisierung pro Block zeigten unsere Daten eine schwache positiv-lineare Korrelation zwischen µ Power und MEP-Amplitude, welche somit im Widerspruch zur angenommenen hemmenden Wirkung von µ steht, aber mittlerweile in mehreren anderen Studien repliziert wurde. Diese Diskrepanz kann z.B. durch eine tatsächlich fazilitatorische Wirkung erklärt werden, oder auch durch eine anatomisch dem sensorischen Kortex (S1) zuzuordnende Quelle der angesteuerten µ-Wellen, was über hem- 83mende Interneurone von S1 auf M1 zu einer ‘Vorzeichenumkehrung’ der Effektrichtung führen könnte. Weiterhin wird eine Abhängigkeit der ‘erregbarsten’ Power-Werte von der Stimulusstärke diskutiert. Im Hauptexperiment sollte mit ‘paarig-assoziativer Stimulation’ (PAS) (intervallsensitive Kombination von Elektrostimulation des rechten Nervus medianus mit TMS des linken M1) positive Plastizität (die Intervention überdauernde Stärkung von Synapsen) induziert werden. Dem ging ein umfangreiches “Screening” zur Identifikation geeigneter Probanden mit ausgeprägtem µ-Rhythmus (für präzise EEGTriggerung) voraus. Letztlich absolvierten 16 Teilnehmer je 4 Sitzungen (eine pro Trigger-Bedingung). Unsere Hypothese war hierbei, mehr Plastizität nach Stimulation während der Tiefpunkte (“Troughs”) als während der Peaks zu erzielen, also mehr synaptische ‘Formbarkeit’ während höherer Erregbarkeit. In Anbetracht der schwachen Ergebnisse des Vorexperiments sowie einer widersprüchlichen Beweislage bezüglich einer fazilitatorischen oder inhibitorischen Funktion wurden hohe und niedrige Power nicht explizit miteinander verglichen. TMS während PAS wurde durch (1) µ-Peaks, (2) µ-Troughs, (3) mittlere µ-Power und (4) open-loop getriggert. (3) und (4) dienten jeweils als Kontrollbedingung. PAS konnte, unabhängig von der EEG-Bedingung, keine signifikante Veränderung der MEP-Amplituden vom Ausgangswert hervorrufen. Die fehlende Wirkung könnte durch intra- und interindividuelle Schwankungen gewisser Parameter zwischen den Sitzungen erklärt werden (z.B. MEP-Ausgangswerte, absolute µ-Power während PAS), die sich jedoch nicht als systematische Confounder zwischen EEG-Bedingungen herausstellten. Die, im Gegensatz zu open-loop-Studien, schwankenden ITIs während der PAS könnten die Wirkung ebenfalls beeinträchtigt haben. Weiterhin waren zwei verschiedene Kortexareale (S1 und M1) am Protokoll beteiligt, was die Identifikation einer relevanten EEG-Eigenschaft erschwerte. Gegenwärtig rufen Plastizitäts-induzierende TMS-Protokolle in der Forschung und in Studien mit Schlaganfallpatienten schwankende und zeitlich begrenzte Wirkungen hervor. Durch EEG-Triggerung und / oder die Kombination mit klassischer Physiotherapie könnte eine verbesserte Effektivität und somit eine routinemäßige Anwendung erreicht werden. Trotz unserer negativen Ergebnisse bleibt EEG-getriggerte TMS ein vielversprechendes Instrument in Forschung und Klinik.This thesis presents two experiments employing real-time EEG-triggered transcranial magnetic stimulation (TMS) on healthy volunteers to investigate the role of sensorimotor 8-14Hz µ rhythm in EEG at rest on corticospinal excitability and induction of positive plasticity. We intended to identify brain states favorable to induction of positive plasticity to inform development of more efficient TMS protocols for clinical application e.g. in stroke patients. Applying TMS triggered by pre-determined EEG brain states in real time (opposed to open-loop TMS with post-hoc trial sorting) offers not only more precise research into the role of certain brain waves, but also translation into more efficient therapies. The membrane potential of superficial cortical neurons fluctuates rhythmically, visible as oscillations in surface EEG. Different brain areas seem to communicate through these synchronized fluctuations. ‘Brain waves’ therefore convey valuable information about the excitability of said areas. Oscillations in the alpha frequency range (8-14Hz) play a crucial role in this, gating information by inhibiting brain areas irrelevant to the current task. According to an influential hypothesis, this function is exerted as an ‘asymmetric pulsed inhibition’, with a maximum of inhibition during the peaks and during high alpha power (∼ amplitude). Sensorimotor alpha frequency waves (µ rhythm) play a similar role as the well-researched occipital alpha does for the visual cortex. The primary motor cortex (M1) provides a quantifiable measure of (corticospinal) excitability, the amplitude of TMS-elicited contralateral muscle twitches (appearing as MEPs in the EMG). The first experiment investigated the role of µ power for M1 excitability. 16 participants underwent one session of single-pulse TMS of the left M1, triggered by overall 10 individual power deciles in pseudorandomized order, partitioned into 4 ‘blocks’ of stimulation over time. The data revealed, after stratification for confounding inter-trial-intervals (ITIs) and normalization to block average, a weak positive linear relationship contrary to the proposed inhibitory role of µ, which has however since been replicated several times in other studies. This discrepancy can be explained e.g. by an in fact facilitatory nature of µ, by a postcentral and thus sensory cortical (S1) source of the targeted oscillations, reversing the inhibitory effect in sign to a facilitatory one through S1-to-M1 feedforward inhibition, or by a shift of most excitable power values dependent on stimulus strength. For the main experiment, we applied a paired associative stimulation (PAS) pro- 81tocol intended to induce positive plasticity (strengthening of synaptic connection outlasting the intervention), combining electrical stimulation of the right median nerve at the wrist with a TMS of the left M1 in a temporally sensitive manner. After an extensive screening to pre-select suitable subjects with a sufficiently strong µ rhythm (to ensure accurate performance of the real-time EEG targeting), 16 participants completed 4 sessions (one condition each). We expected to induce more positive plasticity during more excitable brain states, i.e., µ troughs rather than µ peaks. In light of our findings on µ power from the first experiment (weak influence as compared to ITIs and intrinsic variability over time) and overall contradictory evidence as to its (facilitatory versus inhibitory) role, high vs. low power were not explicitly compared. TMS during PAS was applied at (1) µ peaks, (2) µ troughs, (3) at medium µ powers and (4) open-loop. (3) and (4) both served as controls. The intervention failed to evoke a significant change in MEP amplitudes from baseline irrespective of condition. Possible explanations can be found in the intra- and interindividual variability of decisive parameters across sessions (e.g. baseline amplitudes and absolute µ powers during PAS), which however did not significantly depend on the targeted condition and were thus not true confounders. The number of sessions might still have introduced a further measure of variability. Varying PAS ITIs (due to EEG-triggering) could have also impeded plasticity induction, and the involvement of two cortical regions (S1 and M1) might have complicated the identification of one relevant brain state. Currently, plasticity-inducing TMS protocols in research and clinical trials evoke variable and transient effects. Improvements to enable routine application might come from EEG-triggering and/or combining with traditional motor training (physiotherapy). Regardless of our nil results in plasticity induction, EEG-triggered TMS remains a promising instrument in research and therapy