Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Memristor models for machine learning

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio

A Unified Learning Platform for Dynamic Frequency Scaling in Pipelined Processors

A machine learning (ML) design framework is proposed for dynamically adjusting clock frequency based on propagation delay of individual instructions. A Random Forest model is trained to classify propagation delays in real-time, utilizing current operation type, current operands, and computation history as ML features. The trained model is implemented in Verilog as an additional pipeline stage within a baseline processor. The modified system is simulated at the gate-level in 45 nm CMOS technology, exhibiting a speed-up of 68% and energy reduction of 37% with coarse-grained ML classification. A speed-up of 95% is demonstrated with finer granularities at additional energy costs

Reconstructing Dynamical Systems From Stochastic Differential Equations to Machine Learning

Die Modellierung komplexer Systeme mit einer großen Anzahl von Freiheitsgraden ist in den letzten Jahrzehnten zu einer großen Herausforderung geworden. In der Regel werden nur einige wenige Variablen komplexer Systeme in Form von gemessenen Zeitreihen beobachtet, während die meisten von ihnen - die möglicherweise mit den beobachteten Variablen interagieren - verborgen bleiben. In dieser Arbeit befassen wir uns mit dem Problem der Rekonstruktion und Vorhersage der zugrunde liegenden Dynamik komplexer Systeme mit Hilfe verschiedener datengestützter Ansätze. Im ersten Teil befassen wir uns mit dem umgekehrten Problem der Ableitung einer unbekannten Netzwerkstruktur komplexer Systeme, die Ausbreitungsphänomene widerspiegelt, aus beobachteten Ereignisreihen. Wir untersuchen die paarweise statistische Ähnlichkeit zwischen den Sequenzen von Ereigniszeitpunkten an allen Knotenpunkten durch Ereignissynchronisation (ES) und Ereignis-Koinzidenz-Analyse (ECA), wobei wir uns auf die Idee stützen, dass funktionale Konnektivität als Stellvertreter für strukturelle Konnektivität dienen kann. Im zweiten Teil konzentrieren wir uns auf die Rekonstruktion der zugrunde liegenden Dynamik komplexer Systeme anhand ihrer dominanten makroskopischen Variablen unter Verwendung verschiedener stochastischer Differentialgleichungen (SDEs). In dieser Arbeit untersuchen wir die Leistung von drei verschiedenen SDEs - der Langevin-Gleichung (LE), der verallgemeinerten Langevin-Gleichung (GLE) und dem Ansatz der empirischen Modellreduktion (EMR). Unsere Ergebnisse zeigen, dass die LE bessere Ergebnisse für Systeme mit schwachem Gedächtnis zeigt, während sie die zugrunde liegende Dynamik von Systemen mit Gedächtniseffekten und farbigem Rauschen nicht rekonstruieren kann. In diesen Situationen sind GLE und EMR besser geeignet, da die Wechselwirkungen zwischen beobachteten und unbeobachteten Variablen in Form von Speichereffekten berücksichtigt werden. Im letzten Teil dieser Arbeit entwickeln wir ein Modell, das auf dem Echo State Network (ESN) basiert und mit der PNF-Methode (Past Noise Forecasting) kombiniert wird, um komplexe Systeme in der realen Welt vorherzusagen. Unsere Ergebnisse zeigen, dass das vorgeschlagene Modell die entscheidenden Merkmale der zugrunde liegenden Dynamik der Klimavariabilität erfasst.Modeling complex systems with large numbers of degrees of freedom have become a grand challenge over the past decades. Typically, only a few variables of complex systems are observed in terms of measured time series, while the majority of them – which potentially interact with the observed ones - remain hidden. Throughout this thesis, we tackle the problem of reconstructing and predicting the underlying dynamics of complex systems using different data-driven approaches. In the first part, we address the inverse problem of inferring an unknown network structure of complex systems, reflecting spreading phenomena, from observed event series. We study the pairwise statistical similarity between the sequences of event timings at all nodes through event synchronization (ES) and event coincidence analysis (ECA), relying on the idea that functional connectivity can serve as a proxy for structural connectivity. In the second part, we focus on reconstructing the underlying dynamics of complex systems from their dominant macroscopic variables using different Stochastic Differential Equations (SDEs). We investigate the performance of three different SDEs – the Langevin Equation (LE), Generalized Langevin Equation (GLE), and the Empirical Model Reduction (EMR) approach in this thesis. Our results reveal that LE demonstrates better results for systems with weak memory while it fails to reconstruct underlying dynamics of systems with memory effects and colored-noise forcing. In these situations, the GLE and EMR are more suitable candidates since the interactions between observed and unobserved variables are considered in terms of memory effects. In the last part of this thesis, we develop a model based on the Echo State Network (ESN), combined with the past noise forecasting (PNF) method, to predict real-world complex systems. Our results show that the proposed model captures the crucial features of the underlying dynamics of climate variability

Baseline Data from Servo Motors in a Robotic Arm for Autonomous Machine Fault Diagnosis

Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: low lubrication, no lubrication, two gears chipped, and four gears chipped. Each fault was also examined at 180, 135, 90, and 45-degree swings of the servo arm. Component level data was obtained using an Arduino microcontroller and a feedback wire in each servomotor to obtain the actual position of the servo arm, which allowed for the calculation of the difference in actual and theoretical position and the speed of the servo arm at the various faults. System level data was obtained using OptiTrack’s motion tracking software, Motive, to track the position of two reflective markers on the hand of the robotic arm. At the component level, the low lubrication and no lubrication faults did not exhibit a large difference from the normal servomotor, whereas the servomotors with the gears chipped exhibited significant differences when compared to the normal servomotor. When evaluating the difference in position and speed of the servo arm at larger degree sweeps it was more evident that failure occurred, as opposed to the data at smaller degree sweeps. At the system level, the error was not as visible in the data as there wasn’t much distinction between the speeds of the robotic arm’s hand when the servomotors with faults were placed in it. The results of this work indicate that servomotors can be used to create fault behavior datasets at the component and system level that are usable for machine learning