Publications

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We have developed a pulsed optically pumped magnetometer (OPM) array for detecting magnetic field maps originated from an arbitrary current distribution. The presented magnetic source imaging (MSI) system features 24-OPM channels has a data rate of 500 S/s, a sensitivity of 0.8\mathrm {pT/}\sqrt {\mathrm {Hz}} , and a dynamic range of 72 dB. We have employed our pulsed-OPM MSI system for measuring the magnetic field map of a test coil structure. The coils are moved across the array in an indexed fashion to measure the magnetic field over an area larger than the array. The captured magnetic field maps show excellent agreement with the simulation results. Assuming a 2-D current distribution, we have solved the inverse problem using the measured magnetic field maps, and the reconstructed current distribution image is compared with that of the simulation.

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.

Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. The experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data are sparse. In addition, we show that the energy cost associated with our approach is competitive to that of the sequential method.

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expression (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities.In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naïve solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naïve solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.

Neural-inspired spike-based computing machines often claim to achieve considerable advantages in terms of energy and time efficiency by using spikes for computation and communication. However, fundamental questions about spike-based computation remain unanswered. For instance, how much advantage do spike-based approaches have over conventionalmethods, and underwhat circumstances does spike-based computing provide a comparative advantage? Simply implementing existing algorithms using spikes as the medium of computation and communication is not guaranteed to yield an advantage. Here, we demonstrate that spike-based communication and computation within algorithms can increase throughput, and they can decrease energy cost in some cases. We present several spiking algorithms, including sorting a set of numbers in ascending/descending order, as well as finding the maximum or minimum ormedian of a set of numbers.We also provide an example application: a spiking median-filtering approach for image processing providing a low-energy, parallel implementation. The algorithms and analyses presented here demonstrate that spiking algorithms can provide performance advantages and offer efficient computation of fundamental operations useful in more complex algorithms.

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File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features, such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used to reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers, such as support vector machines over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.

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Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent advances in large-scale neural computing hardware has made their practical implementation a near-term possibility. We describe a theoretical approach for multiplying two N by N matrices that integrates threshold gate logic with conventional fast matrix multiplication algorithms, that perform O(Nω) arithmetic operations for a positive constant ω < 3. Our approach converts such a fast matrix multiplication algorithm into a constant-depth threshold circuit with approximately O(Nω) gates. Prior to our work, it was not known whether the Θ(N3)-gate barrier for matrix multiplication was surmountable by constant-depth threshold circuits. Dense matrix multiplication is a core operation in convolutional neural network training. Performing this work on a neural architecture instead of off-loading it to a GPU may be an appealing option.

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Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO_x, and two conducting metallization systems, Cu-SiO₂, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.

With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose the piecewise empirical model (PEM), an empirically derived general purpose compact model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance-switching device technologies. In this work, we explain the details of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific Ta / TaO x device.

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaOx, and two conducting metallization systems, Cu-SiO2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. This suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.

Unlike general purpose computer architectures that are comprised of complex processor cores and sequential computation, the brain is innately parallel and contains highly complex connections between computational units (neurons). Key to the architecture of the brain is a functionality enabled by the combined effect of spiking communication and sparse connectivity with unique variable efficacies and temporal latencies. Utilizing these neuroscience principles, we have developed the Spiking Temporal Processing Unit (STPU) architecture which is well-suited for areas such as pattern recognition and natural language processing. In this paper, we formally describe the STPU, implement the STPU on a field programmable gate array, and show measured performance data.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

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This presentation describes how liquid states can be observed using a crossbar device. Models, tests, errors, and other results are described here.

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Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaOx devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaOx devices achieve ideal numeric accuracy. In addition, low noise, linear Li1-xCoO2 devices train to ideal numeric accuracies using periodic carry on both datasets.

Miniature ultrasonic lysis for biological sample preparation is a promising technique for efficient and rapid extraction of nucleic acids and proteins from a wide variety of biological sources. Acoustic methods achieve rapid, unbiased, and efficacious disruption of cellular membranes while avoiding the use of harsh chemicals and enzymes, which interfere with detection assays. In this work, a miniature acoustic nucleic acid extraction system is presented. Using a miniature bulk acoustic wave (BAW) transducer array based on 36° Y-cut lithium niobate, acoustic waves were coupled into disposable laminate-based microfluidic cartridges. To verify the lysing effectiveness, the amount of liberated ATP and the cell viability were measured and compared to untreated samples. The relationship between input power, energy dose, flow-rate, and lysing efficiency were determined. DNA was purified on-chip using three approaches implemented in the cartridges: a silica-based sol-gel silica-bead filled microchannel, nucleic acid binding magnetic beads, and Nafion-coated electrodes. Using E. coli, the lysing dose defined as ATP released per joule was 2.2× greater, releasing 6.1× more ATP for the miniature BAW array compared to a bench-top acoustic lysis system. An electric field-based nucleic acid purification approach using Nafion films yielded an extraction efficiency of 69.2% in 10 min for 50 μL samples.

In 2016, Lewis Rhodes Labs, (LRL), shipped the first commercially viable Neuromorphic Processing Unit, (NPU), branded as a Neuromorphic Data Microscope (NDM). This product leverages architectural mechanisms derived from the sensory cortex of the human brain to efficiently implement pattern matching. LRL and Sandia National Labs have optimized this product for streaming analytics, and demonstrated a 1,000x power per operation reduction in an FPGA format. When reduced to an ASIC, the efficiency will improve to 1,000,000x. Additionally, the neuromorphic nature of the device gives it powerful computational attributes that are counterintuitive to those schooled in traditional von Neumann architectures. The Neuromorphic Data Microscope is the first of a broad class of brain-inspired, time domain processors that will profoundly alter the functionality and economics of data processing.

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Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.

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.

Considerable effort is currently being spent designing neuromorphic hardware for addressing challenging problems in a variety of pattern-matching applications. These neuromorphic systems offer low power architectures with intrinsically parallel and simple spiking neuron processing elements. Unfortunately, these new hardware architectures have been largely developed without a clear justification for using spiking neurons to compute quantities for problems of interest. Specifically, the use of spiking for encoding information in time has not been explored theoretically with complexity analysis to examine the operating conditions under which neuromorphic computing provides a computational advantage (time, space, power, etc.) In this paper, we present and formally analyze the use of temporal coding in a neural-inspired algorithm for optimization-based computation in neural spiking architectures.

Resistive memory crossbars can dramatically reduce the energy required to perform computations in neural algorithms by three orders of magnitude when compared to an optimized digital ASIC [1]. For data intensive applications, the computational energy is dominated by moving data between the processor, SRAM, and DRAM. Analog crossbars overcome this by allowing data to be processed directly at each memory element. Analog crossbars accelerate three key operations that are the bulk of the computation in a neural network as illustrated in Fig 1: vector matrix multiplies (VMM), matrix vector multiplies (MVM), and outer product rank 1 updates (OPU)[2]. For an NxN crossbar the energy for each operation scales as the number of memory elements O(N2) [2]. This is because the crossbar performs its entire computation in one step, charging all the capacitances only once. Thus the CV2 energy of the array scales as array size. This fundamentally better than trying to read or write a digital memory. Each row of any NxN digital memory must be accessed one at a time, resulting in N columns of length O(N) being charged N times, requiring O(N3) energy to read a digital memory. Thus an analog crossbar has a fundamental O(N) energy scaling advantage over a digital system. Furthermore, if the read operation is done at low voltage and is therefore noise limited, the read energy can even be independent of the crossbar size, O(1) [2].

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The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted theory suggests the dentate gyrus performs pattern separation-similar inputs yield decorrelated outputs. Although an active region of study and theory, few logically rigorous arguments detail the dentate gyrus's (DG) coding.We suggest a theoretically tractable, combinatorial model for this action. The model provides formal methods for a highly redundant, arbitrarily sparse, and decorrelated output signal. To explore the value of this model framework, we assess how suitable it is for two notable aspects of DG coding: how it can handle the highly structured grid cell representation in the input entorhinal cortex region and the presence of adult neurogenesis, which has been proposed to produce a heterogeneous code in the DG.We find tailoring themodel to grid cell input yields expansion parameters consistent with the literature. In addition, the heterogeneous coding reflects activity gradation observed experimentally. Finally,we connect this approach with more conventional binary threshold neural circuit models via a formal embedding.

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

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For decades, neural networks have shown promise for next-generation computing, and recent breakthroughs in machine learning techniques, such as deep neural networks, have provided state-of-the-art solutions for inference problems. However, these networks require thousands of training processes and are poorly suited for the precise computations required in scientific or similar arenas. The emergence of dedicated spiking neuromorphic hardware creates a powerful computational paradigm which can be leveraged towards these exact scientific or otherwise objective computing tasks. We forego any learning process and instead construct the network graph by hand. In turn, the networks produce guaranteed success often with easily computable complexity. We demonstrate a number of algorithms exemplifying concepts central to spiking networks including spike timing and synaptic delay. We also discuss the application of cross-correlation particle image velocimetry and provide two spiking algorithms; one uses time-division multiplexing, and the other runs in constant time.

The effort to develop larger-scale computing systems introduces a set of related challenges: Large machines are more difficult to synchronize. The sheer quantity of hardware introduces more opportunities for errors. New approaches to hardware, such as low-energy or neuromorphic devices are not directly programmable by traditional methods. These three challenges may be addressed, at least for a subset of interesting problems, by a dynamical systems approach. The initial state of system represents the problem, and the final state of the system represents the solution. By carefully controlling the attractive basin of the system, we can move it between these two points while tolerating errors, which appear as perturbations. Here we describe both conventional and neural computers as dynamical systems, and show how to construct algorithms with resilience to noise, using traditional numerical problems as a special case. This suggests a reduction from numerical problems to spiking neural hardware such as IBM's TrueNorth.

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Through various means of structural and synaptic plasticity enabling online learning, neural networks are constantly reconfiguring their computational functionality. Neural information content is embodied within the configurations, representations, and computations of neural networks. To explore neural information content, we have developed metrics and computational paradigms to quantify neural information content. We have observed that conventional compression methods may help overcome some of the limiting factors of standard information theoretic techniques employed in neuroscience, and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles. Using Lempel-Ziv compression we are able to assess the rate of generation of new patterns across a neural ensemble's firing activity over time to approximate the information content encoded by a neural circuit. As a specific case study, we have been investigating the effect of neural mixed coding schemes due to hippocampal adult neurogenesis.

Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.

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We have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single molecule superresolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet.

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The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

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As transistors start to approach fundamental limits and Moore's law slows down, new devices and architectures are needed to enable continued performance gains. New approaches based on RRAM (resistive random access memory) or memristor crossbars can enable the processing of large amounts of data[1, 2]. One of the most promising applications for RRAM crossbars is brain inspired or neuromorphic computing[3, 4].

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The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. An added challenge arises when the problem domain is dynamic or non-stationary with the data distributions or categorizations changing over time. This phenomenon is known as concept drift. Game-theoretic algorithms are often iterative by nature, consisting of repeated game play rather than a single interaction. Effectively, rather than requiring extensive retraining to update a learning model, a game-theoretic approach can adjust strategies as a novel approach to concept drift. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in an adaptive manner with repeated play to address concept drift, and show results of applying this algorithm to synthetic as well as real data.

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Recent cyber security events have demonstrated the need for algorithms that adapt to the rapidly evolving threat landscape of complex network systems. In particular, human analysts often fail to identify data exfiltration when it is encrypted or disguised as innocuous data. Signature-based approaches for identifying data types are easily fooled and analysts can only investigate a small fraction of network events. However, neural networks can learn to identify subtle patterns in a suitably chosen input space. To this end, we have developed a signal processing approach for classifying data files which readily adapts to new data formats. We evaluate the performance for three input spaces consisting of the power spectral density, byte probability distribution and sliding-window entropy of the byte sequence in a file. By combining all three, we trained a deep neural network to discriminate amongst nine common data types found on the Internet with 97.4% accuracy.

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This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

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The document is a progress report on a first generation rapid testing microfluidic device with integrated sidewall mirrors being conducted by the Sandia National Laboratories team.

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This report provides a detailed overview of the work performed for project number 130781, 'A Systems Biology Approach to Understanding Viral Hemorrhagic Fever Pathogenesis.' We report progress in five key areas: single cell isolation devices and control systems, fluorescent cytokine and transcription factor reporters, on-chip viral infection assays, molecular virology analysis of Arenavirus nucleoprotein structure-function, and development of computational tools to predict virus-host protein interactions. Although a great deal of work remains from that begun here, we have developed several novel single cell analysis tools and knowledge of Arenavirus biology that will facilitate and inform future publications and funding proposals.

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Single-cell analysis offers a promising method of studying cellular functions including investigation of mechanisms of host-pathogen interaction. We are developing a microfluidic platform that integrates single-cell capture along with an optimized interface for high-resolution fluorescence microscopy. The goal is to monitor, using fluorescent reporter constructs and labeled antibodies, the early events in signal transduction in innate immunity pathways of macrophages and other immune cells. The work presented discusses the development of the single-cell capture device, the iCellator chip, that isolates, captures, and exposes cells to pathogenic insults. We have successfully monitored the translocation of NF-κB, a transcription factor, from the cytoplasm to the nucleus after lipopolysaccharide (LPS) stimulation of RAW264.7 macrophages.

We present MEMS polysilicon microvalves for flow control of a rapid analytical microsystem (Micro-Gas-Analyzer, MGA). All valve components (boss, seat, springs, electrodes, and stops) are surface micromachined in the SUMMiT™ microfabrication process. The valves have been characterized at high flow rate when open (60 ml/min air), low leak rate when closed (<0.0025 ml/min Hydrogen, H2), and tunable closing pressures (1 to 35 psig). Active electrostatic valves have been shown to hold closed (voltage on) against a high pressure (>40 psig) for sample loading, open for gas chromatograph (GC) loading (voltage off), and reclose against low pressure 2-5 psig. ©2009 IEEE.

We have successfully developed a nucleic acid extraction system based on a microacoustic lysis array coupled to an integrated nucleic acid extraction system all on a single cartridge. The microacoustic lysing array is based on 36{sup o} Y cut lithium niobate, which couples bulk acoustic waves (BAW) into the microchannels. The microchannels were fabricated using Mylar laminates and fused silica to form acoustic-fluidic interface cartridges. The transducer array consists of four active elements directed for cell lysis and one optional BAW element for mixing on the cartridge. The lysis system was modeled using one dimensional (1D) transmission line and two dimensional (2D) FEM models. For input powers required to lyse cells, the flow rate dictated the temperature change across the lysing region. From the computational models, a flow rate of 10 {micro}L/min produced a temperature rise of 23.2 C and only 6.7 C when flowing at 60 {micro}L/min. The measured temperature changes were 5 C less than the model. The computational models also permitted optimization of the acoustic coupling to the microchannel region and revealed the potential impact of thermal effects if not controlled. Using E. coli, we achieved a lysing efficacy of 49.9 {+-} 29.92 % based on a cell viability assay with a 757.2 % increase in ATP release within 20 seconds of acoustic exposure. A bench-top lysing system required 15-20 minutes operating up to 58 Watts to achieve the same level of cell lysis. We demonstrate that active mixing on the cartridge was critical to maximize binding and release of nucleic acid to the magnetic beads. Using a sol-gel silica bead matrix filled microchannel the extraction efficacy was 40%. The cartridge based magnetic bead system had an extraction efficiency of 19.2%. For an electric field based method that used Nafion films, a nucleic acid extraction efficiency of 66.3 % was achieved at 6 volts DC. For the flow rates we tested (10-50 {micro}L/min), the nucleic acid extraction time was 5-10 minutes for a volume of 50 {micro}L. Moreover, a unique feature of this technology is the ability to replace the cartridges for subsequent nucleic acid extractions.

A key aspect of decision making is determining when errors or conflicts exist in information and knowing whether to continue or terminate an action. Understanding the error-conflict processing is crucial in order to emulate higher brain functions in hardware and software systems. Specific brain regions, most notably the anterior cingulate cortex (ACC) are known to respond to the presence of conflicts in information by assigning a value to an action. Essentially, this conflict signal triggers strategic adjustments in cognitive control, which serve to prevent further conflict. The most probable mechanism is the ACC reports and discriminates different types of feedback, both positive and negative, that relate to different adaptations. Unique cells called spindle neurons that are primarily found in the ACC (layer Vb) are known to be responsible for cognitive dissonance (disambiguation between alternatives). Thus, the ACC through a specific set of cells likely plays a central role in the ability of humans to make difficult decisions and solve challenging problems in the midst of conflicting information. In addition to dealing with cognitive dissonance, decision making in high consequence scenarios also relies on the integration of multiple sets of information (sensory, reward, emotion, etc.). Thus, a second area of interest for this proposal lies in the corticostriatal networks that serve as an integration region for multiple cognitive inputs. In order to engineer neurological decision making processes in silicon devices, we will determine the key cells, inputs, and outputs of conflict/error detection in the ACC region. The second goal is understand in vitro models of corticostriatal networks and the impact of physical deficits on decision making, specifically in stressful scenarios with conflicting streams of data from multiple inputs. We will elucidate the mechanisms of cognitive data integration in order to implement a future corticostriatal-like network in silicon devices for improved decision processing.

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Columbia University has developed a sensitive highly multiplexed system for genetic identification of nucleic acid targets. The primary obstacle to implementing this technology is the high rate of false positives due to high levels of unbound reporters that remain within the system after hybridization. The ability to distinguish between free reporters and reporters bound to targets limits the use of this technology. We previously demonstrated a new electrokinetic method for binary separation of kb pair long DNA molecules and oligonucleotides. The purpose of this project 99864 is to take these previous demonstrations and further develop the technique and hardware for field use. Specifically, our objective was to implement separation in a heterogeneous sample (containing target DNA and background oligo), to perform the separation in a flow-based device, and to develop all of the components necessary for field testing a breadboard prototype system.

Abstract not provided.

A variety of fabrication techniques have been used to make microfluidic microsystems: bulk etching in silicon and glass, plastic molding and machining, and PDMS (silicone) casting. Surprisingly the most widely used method of integrated circuit (IC) fabrication (surface micromachining - SMM) has not been extensively utilized in microfluidics despite its wide use in MEMS. There are economic reasons that SMM is not often used in microfluidics; high infrastructure and start-up costs and relatively long fabrication times: and there are technical reasons; packaging difficulties, dominance of surface forces, and fluid volume scaling issues. However, there are also important technical and economic advantages for SMM microfluidics relating to large-scale batch, no-assembly fabrication, and intimate integration of mechanical, electrical, microfluidic, and nano-scale sub-systems on one chip. In our work at Sandia National Laboratories MDL (Microelectronics Development Lab) we have built on the existing MEMS SMM infrastructure to produce a variety of microfluidic microsystems. These example microsystems illustrate the challenges and opportunities associated with SMM microfluidics. In this paper we briefly discuss two SMM microfluidic microsystems (made in the SUMMiT™ and SwIFT™ processes - www.mdl.sandia.gov/micromachine ) in terms of technical challenges and unique SMM microfluidics opportunities. The two example microsystems are a DEP (dielectrophoretic) trap, and a drop ejector patterning system. Copyright © 2005 by ASME.

This report contains the summary of LDRD project 91312, titled ''Binary Electrokinetic Separation of Target DNA from Background DNA Primers''. This work is the first product of a collaboration with Columbia University and the Northeast BioDefense Center of Excellence. In conjunction with Ian Lipkin's lab, we are developing a technique to reduce false positive events, due to the detection of unhybridized reporter molecules, in a sensitive and multiplexed detection scheme for nucleic acids developed by the Lipkin lab. This is the most significant problem in the operation of their capability. As they are developing the tools for rapidly detecting the entire panel of hemorrhagic fevers this technology will immediately serve an important national need. The goal of this work was to attempt to separate nucleic acid from a preprocessed sample. We demonstrated the preconcentration of kilobase-pair length double-stranded DNA targets, and observed little preconcentration of 60 base-pair length single-stranded DNA probes. These objectives were accomplished in microdevice formats that are compatible with larger detection systems for sample pre-processing. Combined with Columbia's expertise, this technology would enable a unique, fast, and potentially compact method for detecting/identifying genetically-modified organisms and multiplexed rapid nucleic acid identification. Another competing approach is the DARPA funded IRIS Pharmaceutical TIGER platform which requires many hours for operation, and an 800k$ piece of equipment that fills a room. The Columbia/SNL system could provide a result in 30 minutes, at the cost of a few thousand dollars for the platform, and would be the size of a shoebox or smaller.

Abstract not provided.

Abstract not provided.

The objective of this LDRD was to develop microdevice strategies for dealing with samples to be examined in biological detection systems. This includes three sub-components: namely, microdevice fabrication, sample delivery to the microdevice, and sample processing within the microdevice. The first component of this work focused on utilizing Sandia's surface micromachining technology to fabricate small volume (nanoliter) fluidic systems for processing small quantities of biological samples. The next component was to develop interfaces for the surface-micromachined silicon devices. We partnered with Micronics, a commercial company, to produce fluidic manifolds for sample delivery to our silicon devices. Pressure testing was completed to examine the strength of the bond between the pressure-sensitive adhesive layer and the silicon chip. We are also pursuing several other methods, both in house and external, to develop polymer-based fluidic manifolds for packaging silicon-based microfluidic devices. The second component, sample processing, is divided into two sub-tasks: cell collection and cell lysis. Cell collection was achieved using dielectrophoresis, which employs AC fields to collect cells at energized microelectrodes, while rejecting non-cellular particles. Both live and dead Staph. aureus bacteria have been collected using RF frequency dielectrophoresis. Bacteria have been separated from polystyrene microspheres using frequency-shifting dielectrophoresis. Computational modeling was performed to optimize device separation performance, and to predict particle response to the dielectrophoretic traps. Cell lysis is continuing to be pursued using microactuators to mechanically disrupt cell membranes. Novel thermal actuators, which can generate larger forces than previously tested electrostatic actuators, have been incorporated with and tested with cell lysis devices. Significant cell membrane distortion has been observed, but more experiments need to be conducted to determine the effects of the observed distortion on membrane integrity and cell viability. Finally, we are using a commercial PCR DNA amplification system to determine the limits of detectable sample size, and to examine the amplification of DNA bound to microspheres. Our objective is to use microspheres as capture-and-carry chaperones for small molecules such as DNA and proteins, enabling the capture and concentration of the small molecules using dielectrophoresis. Current tests demonstrated amplification of DNA bound to micron-sized polystyrene microspheres using 20-50 microliter volume size reactions.

This report contains the summary of the 'Magnetophoretic Bead Trapping in a High-Flowrate Biological Detection System' LDRD project 74795. The objective of this project is to develop a novel biodetection system for high-throughput sample analysis. The chief application of this system is in detection of very low concentrations of target molecules from a complex liquid solution containing many different constituents--some of which may interfere with identification of the target molecule. The system is also designed to handle air sampling by using an aerosol system (for instance a WESP - Wet Electro-Static Precipitator, or an impact spray system) to get air sample constituents into the liquid volume. The system described herein automatically takes the raw liquid sample, whether air converted or initially liquid matrix, and mixes in magnetic detector beads that capture the targets of interest and then performs the sample cleanup function, allowing increased sensitivity and eliminating most false positives and false negatives at a downstream detector. The surfaces of the beads can be functionalized in a variety of ways in order to maximize the number of targets to be captured and concentrated. Bacteria and viruses are captured using antibodies to surface proteins on bacterial cell walls or viral particle coats. In combination with a cell lysis or PCR (Polymerase Chain Reaction), the beads can be used as a DNA or RNA probe to capture nucleic acid patterns of interest. The sample cleanup capability of this system would allow different raw biological samples, such as blood or saliva to be analyzed for the presence of different infectious agents (e.g. smallpox or SARS). For future studies, we envision functionalizing bead surfaces to bind to chemical weapons agents, radio-isotopes, and explosives. The two main objectives of this project were to explore methods for enhancing the mixing of the capture microspheres in the sample, and to develop a novel high-throughput magnetic microsphere trap. We have developed a novel technique using the magnetic capture microspheres as 'stirrer bars' in a fluid sample to enhance target binding to the microsphere surfaces. We have also made progress in developing a polymer-MEMS electromagnet for trapping magnetic spheres in a high-flowrate fluid format.

Fast and quantitative analysis of cellular activity, signaling and responses to external stimuli is a crucial capability and it has been the goal of several projects focusing on patch clamp measurements. To provide the maximum functionality and measurement options, we have developed a patch clamp array device that incorporates on-chip electronics, mechanical, optical and microfluidic coupling as well as cell localization through fluid flow. The preliminary design, which integrated microfluidics, electrodes and optical access, was fabricated and tested. In addition, new designs which further combine mechanical actuation, on-chip electronics and various electrode materials with the previous designs are currently being fabricated.

Abstract not provided.

Retinal prosthesis projects around the world have been pursuing a functional replacement system for patients with retinal degeneration. In this paper, the concept for a micromachined conformal electrode array is outlined. Individual electrodes are designed to float on micromachined springs on a substrate that will enable the adjustment of spring constants-and therefore contact force-by adjusting the dimensions of the springs at each electrode. This also allows the accommodation of the varying curvature/topography of the retina. We believe that this approach provides several advantages by improving the electrode/tissue interface as well as generating some new options for in-situ measurements and overall system design.

Retinal prosthesis projects around the world have been pursuing a functional replacement system for patients with retinal degeneration. In this paper, the concept for a micromachined conformal electrode array is outlined. Individual electrodes are designed to float on micromachined springs on a substrate that will enable the adjustment of spring constants-and therefore contact force-by adjusting the dimensions of the springs at each electrode. This also allows the accommodation of the varying curvature/topography of the retina. We believe that this approach provides several advantages by improving the electrode/tissue interface as well as generating some new options for in-situ measurements and overall system design.

Abstract not provided.