A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Parameter identification methods for improving structural dynamic models

There is an increasing need to develop Parameter Identification methods for improving structural dynamic models, based on the inability of engineers to produce mathematical models which correlate with experimental data. This research explores the efficiency of combining Component Mode Synthesis (substructuring) methods with Parameter Identification procedures in order to improve analytical modeling of structural components and their connections. Improvements are computed in terms of physical stiffness and damping parameters in order that the physical characteristics of the model can be better understood. Connections involving both viscous and friction damping are investigated. Substructuring methods are utilized to reduce the complexity of the identification problem. Component and inter-component structural connection properties are evaluated and identified independently, thus simplifying the identification problem. It is shown that modal test data is effective for identifying modeling problems associated with structural components, and for determining the stiffness and damping properties of intercomponent connections. In general, Parameter Identification is improved when greater quantities of experimental data are available

Limit theorems for assortativity and clustering in null models for scale-free networks

An important problem in modeling networks is how to generate a randomly sampled graph with given degrees. A popular model is the configuration model, a network with assigned degrees and random connections. The erased configuration model is obtained when self-loops and multiple edges in the configuration model are removed. We prove an upper bound for the number of such erased edges for regularly-varying degree distributions with infinite variance, and use this result to prove central limit theorems for Pearson's correlation coefficient and the clustering coefficient in the erased configuration model. Our results explain the structural correlations in the erased configuration model and show that removing edges leads to different scaling of the clustering coefficient. We then prove that for the rank-1 inhomogeneous random graph, another null model that creates scale-free simple networks, the results for Pearson's correlation coefficient as well as for the clustering coefficient are similar to the results for the erased configuration model

Discrete Methods in Statistics: Feature Selection and Fairness-Aware Data Mining

This dissertation is a detailed investigation of issues that arise in models that change discretely. Models are often constructed by either including or excluding features based on some criteria. These discrete changes are challenging to analyze due to correlation between features. Feature selection is the problem of identifying an appropriate set of features to include in a model, while fairness-aware data mining is the problem of needing to remove the \emph{influence} of protected features from a model. This dissertation provides frameworks for understanding each problem and algorithms for accomplishing the desired goal. The feature selection problem is addressed through the framework of sequential hypothesis testing. We elucidate the statistical challenges in repeatedly using inference in this domain and demonstrate how current methods fail to address them. Our algorithms build on classically motivated, multiple testing procedures to control measures of false rejections when using hypothesis testing during forward stepwise regression. Furthermore, these methods have much higher power than recent proposals from the conditional inference literature. The fairness-aware data mining community is grappling with fundamental questions concerning fairness in statistical modeling. Tension exists between identifying explainable differences between groups and discriminatory ones. We provide a framework for understanding the connections between fairness and the use of protected information in modeling. With this discussion in hand, generating fair estimates is straight-forward

An Investigation of the Effects of Modeling Application Workloads and Path Characteristics on Network Performance

Network testbeds and simulators remain the dominant platforms for evaluating networking technologies today. Central to the problem of network emulation or simulation is the problem modeling and generating realistic, synthetic Internet traffic as the results of such experiments are valid to the extent that the traffic generated to drive these experiments accurately represents the traffic carried in real production networks. Modeling and generating realistic Internet traffic remains a complex and not well-understood problem in empirical networking research. When modeling production network traffic, researchers lack a clear understanding about which characteristics of the traffic must be modeled, and how these traffic characteristics affect the results of their experiments. In this dissertation, we developed and analyzed a spectrum of empirically-derived traffic models with varying degrees of realism. For TCP traffic, we examined several choices for modeling the internal structure of TCP connections (the pattern of request/response exchanges), and the round trip times of connections. Using measurements from two different production networks, we constructed nine different traffic models, each embodying different choices in the modeling space, and conducted extensive experiments to evaluate these choices on a 10Gbps laboratory testbed. As a result of this study, we demonstrate that the old adage of garbage-in-garbage-out applies to empirical networking research. We conclude that the structure of traffic driving an experiment significantly affects the results of the experiment. And we demonstrate this by showing the effects on four key network performance metrics: connection durations, response times, router queue lengths, and number of active connections in the network

Meshless Mechanics and Point-Based Visualization Methods for Surgical Simulations

Computer-based modeling and simulation practices have become an integral part of the medical education field. For surgical simulation applications, realistic constitutive modeling of soft tissue is considered to be one of the most challenging aspects of the problem, because biomechanical soft-tissue models need to reflect the correct elastic response, have to be efficient in order to run at interactive simulation rates, and be able to support operations such as cuts and sutures. Mesh-based solutions, where the connections between the individual degrees of freedom (DoF) are defined explicitly, have been the traditional choice to approach these problems. However, when the problem under investigation contains a discontinuity that disrupts the connectivity between the DoFs, the underlying mesh structure has to be reconfigured in order to handle the newly introduced discontinuity correctly. This reconfiguration for mesh-based techniques is typically called dynamic remeshing, and most of the time it causes the performance bottleneck in the simulation. In this dissertation, the efficiency of point-based meshless methods is investigated for both constitutive modeling of elastic soft tissues and visualization of simulation objects, where arbitrary discontinuities/cuts are applied to the objects in the context of surgical simulation. The point-based deformable object modeling problem is examined in three functional aspects: modeling continuous elastic deformations with, handling discontinuities in, and visualizing a point-based object. Algorithmic and implementation details of the presented techniques are discussed in the dissertation. The presented point-based techniques are implemented as separate components and integrated into the open-source software framework SOFA. The presented meshless continuum mechanics model of elastic tissue were verified by comparing it to the Hertzian non-adhesive frictionless contact theory. Virtual experiments were setup with a point-based deformable block and a rigid indenter, and force-displacement curves obtained from the virtual experiments were compared to the theoretical solutions. The meshless mechanics model of soft tissue and the integrated novel discontinuity treatment technique discussed in this dissertation allows handling cuts of arbitrary shape. The implemented enrichment technique not only modifies the internal mechanics of the soft tissue model, but also updates the point-based visual representation in an efficient way preventing the use of costly dynamic remeshing operations

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area