Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.

Photonic integrated circuit for the manipulation of coherent optical combs

Photonic integration has become an important research topic in research for applications in the telecommunications industry. Current optical internet infrastructure has reached capacity with current generation dense wavelength division multiplexing (DWDM) systems fully occupying the low absorption region of optical fibre from 1530 nm to 1625 nm (the C and L bands). This is both due to an increase in the number of users worldwide and existing users demanding more bandwidth. Therefore, current research is focussed on using the available telecommunication spectrum more efficiently. To this end, coherent communication systems are being developed. Advanced coherent modulation schemes can be quite complex in terms of the number and array of devices required for implementation. In order to make these systems viable both logistically and commercially, photonic integration is required. In traditional DWDM systems, arrayed waveguide gratings (AWG) are used to both multiplex and demultiplex the multi-wavelength signal involved. AWGs are used widely as they allow filtering of the many DWDM wavelengths simultaneously. However, when moving to coherent telecommunication systems such as coherent optical frequency division multiplexing (OFDM) smaller FSR ranges are required from the AWG. This increases the size of the device which is counter to the miniaturisation which integration is trying to achieve. Much work was done with active filters during the 1980s. This involved using a laser device (usually below threshold) to allow selective wavelength filtering of input signals. By using more complicated cavity geometry devices such as distributed feedback (DFB) and sampled grating distributed Bragg gratings (SG-DBR) narrowband filtering is achievable with high suppression (>30 dB) of spurious wavelengths. The active nature of the devices also means that, through carrier injection, the index can be altered resulting in tunability of the filter. Used above threshold, active filters become useful in filtering coherent combs. Through injection locking, the coherence of the filtered wavelengths with the original comb source is retained. This gives active filters potential application in coherent communication system as demultiplexers. This work will focus on the use of slotted Fabry-Pérot (SFP) semiconductor lasers as active filters. Experiments were carried out to ensure that SFP lasers were useful as tunable active filters. In all experiments in this work the SFP lasers were operated above threshold and so injection locking was the mechanic by which the filters operated. Performance of the lasers under injection locking was examined using both single wavelength and coherent comb injection. In another experiment two discrete SFP lasers were used simultaneously to demultiplex a two-line coherent comb. The relative coherence of the comb lines was retained after demultiplexing. After showing that SFP lasers could be used to successfully demultiplex coherent combs a photonic integrated circuit was designed and fabricated. This involved monolithic integration of a MMI power splitter with an array of single facet SFP lasers. This device was tested much in the same way as the discrete devices. The integrated device was used to successfully demultiplex a two line coherent comb signal whilst retaining the relative coherence between the filtered comb lines. A series of modelling systems were then employed in order to understand the resonance characteristics of the fabricated devices, and to understand their performance under injection locking. Using this information, alterations to the SFP laser designs were made which were theoretically shown to provide improved performance and suitability for use in filtering coherent comb signals.

Optimized, unequal pulse spacing in multiple echo sequences improves refocusing in magnetic resonance.

A recent quantum computing paper (G. S. Uhrig, Phys. Rev. Lett. 98, 100504 (2007)) analytically derived optimal pulse spacings for a multiple spin echo sequence designed to remove decoherence in a two-level system coupled to a bath. The spacings in what has been called a "Uhrig dynamic decoupling (UDD) sequence" differ dramatically from the conventional, equal pulse spacing of a Carr-Purcell-Meiboom-Gill (CPMG) multiple spin echo sequence. The UDD sequence was derived for a model that is unrelated to magnetic resonance, but was recently shown theoretically to be more general. Here we show that the UDD sequence has theoretical advantages for magnetic resonance imaging of structured materials such as tissue, where diffusion in compartmentalized and microstructured environments leads to fluctuating fields on a range of different time scales. We also show experimentally, both in excised tissue and in a live mouse tumor model, that optimal UDD sequences produce different T(2)-weighted contrast than do CPMG sequences with the same number of pulses and total delay, with substantial enhancements in most regions. This permits improved characterization of low-frequency spectral density functions in a wide range of applications.

Surface plasmon resonance in nanostructured metal films under the Kretschmann configuration

We systematically investigated the surface plasmon resonance in one-dimensional (1D) subwavelength nanostructured metal films under the Kretschmann configuration. We calculated the reflectance, transmittance, and absorption for varying the dielectric fill factor, the period of the 1D nanostructure, and the metal film thickness. We have found that the small dielectric slits in the metal films reduce the surface plasmon resonance angle and move it toward the critical angle for total internal reflection. The reduction in surface plasmon resonance angle in nanostructured metal films is due to the increased intrinsic free electron oscillation frequency in metal nanostructures. Also we have found that the increasing the spatial frequency of the 1D nanograting reduces the surface plasmon resonance angle, which indicates that less momentum is needed to match the momentum of the surface plasmon-polariton. The variation in the nanostructured metal film thickness changes the resonance angle slightly, but mainly remains as a mean to adjust the coupling between the incident optical wave and the surface plasmon-polariton wave. © 2009 American Institute of Physics.

Spectral structure of the pygmy dipole resonance.

High-sensitivity studies of E1 and M1 transitions observed in the reaction 138Ba(gamma,gamma{'}) at energies below the one-neutron separation energy have been performed using the nearly monoenergetic and 100% linearly polarized photon beams of the HIgammaS facility. The electric dipole character of the so-called "pygmy" dipole resonance was experimentally verified for excitations from 4.0 to 8.6 MeV. The fine structure of the M1 "spin-flip" mode was observed for the first time in N=82 nuclei.

Magnetic resonance water proton relaxation in protein solutions and tissue: T(1rho) dispersion characterization.

BACKGROUND: Image contrast in clinical MRI is often determined by differences in tissue water proton relaxation behavior. However, many aspects of water proton relaxation in complex biological media, such as protein solutions and tissue are not well understood, perhaps due to the limited empirical data. PRINCIPAL FINDINGS: Water proton T(1), T(2), and T(1rho) of protein solutions and tissue were measured systematically under multiple conditions. Crosslinking or aggregation of protein decreased T(2) and T(1rho), but did not change high-field T(1). T(1rho) dispersion profiles were similar for crosslinked protein solutions, myocardial tissue, and cartilage, and exhibited power law behavior with T(1rho)(0) values that closely approximated T(2). The T(1rho) dispersion of mobile protein solutions was flat above 5 kHz, but showed a steep curve below 5 kHz that was sensitive to changes in pH. The T(1rho) dispersion of crosslinked BSA and cartilage in DMSO solvent closely resembled that of water solvent above 5 kHz but showed decreased dispersion below 5 kHz. CONCLUSIONS: Proton exchange is a minor pathway for tissue T(1) and T(1rho) relaxation above 5 kHz. Potential models for relaxation are discussed, however the same molecular mechanism appears to be responsible across 5 decades of frequencies from T(1rho) to T(1).

Magnetic Resonance Imaging Biomarkers of Renal Structure and Function

The kidney's major role in filtration depends on its high blood flow, concentrating mechanisms, and biochemical activation. The kidney's greatest strengths also lead to vulnerability for drug-induced nephrotoxicity and other renal injuries. The current standard to diagnose renal injuries is with a percutaneous renal biopsy, which can be biased and insufficient. In one particular case, biopsy of a kidney with renal cell carcinoma can actually initiate metastasis. Tools that are sensitive and specific to detect renal disease early are essential, especially noninvasive diagnostic imaging. While other imaging modalities (ultrasound and x-ray/CT) have their unique advantages and disadvantages, MRI has superb soft tissue contrast without ionizing radiation. More importantly, there is a richness of contrast mechanisms in MRI that has yet to be explored and applied to study renal disease.

The focus of this work is to advance preclinical imaging tools to study the structure and function of the renal system. Studies were conducted in normal and disease models to understand general renal physiology as well as pathophysiology. This dissertation is separated into two parts--the first is the identification of renal architecture with ex vivo MRI; the second is the characterization of renal dynamics and function with in vivo MRI. High resolution ex vivo imaging provided several opportunities including: 1) identification of fine renal structures, 2) implementation of different contrast mechanisms with several pulse sequences and reconstruction methods, 3) development of image-processing tools to extract regions and structures, and 4) understanding of the nephron structures that create MR contrast and that are important for renal physiology. The ex vivo studies allowed for understanding and translation to in vivo studies. While the structure of this dissertation is organized by individual projects, the goal is singular: to develop magnetic resonance imaging biomarkers for renal system.

The work presented here includes three ex vivo studies and two in vivo studies:

1) Magnetic resonance histology of age-related nephropathy in sprague dawley.

2) Quantitative susceptibility mapping of kidney inflammation and fibrosis in type 1 angiotensin receptor-deficient mice.

3) Susceptibility tensor imaging of the kidney and its microstructural underpinnings.

4) 4D MRI of renal function in the developing mouse.

5) 4D MRI of polycystic kidneys in rapamycin treated Glis3-deficient mice.

Activation in mesolimbic and visuospatial neural circuits elicited by smoking cues: evidence from functional magnetic resonance imaging.

OBJECTIVE: The authors sought to increase understanding of the brain mechanisms involved in cigarette addiction by identifying neural substrates modulated by visual smoking cues in nicotine-deprived smokers. METHOD: Event-related functional magnetic resonance imaging (fMRI) was used to detect brain activation after exposure to smoking-related images in a group of nicotine-deprived smokers and a nonsmoking comparison group. Subjects viewed a pseudo-random sequence of smoking images, neutral nonsmoking images, and rare targets (photographs of animals). Subjects pressed a button whenever a rare target appeared. RESULTS: In smokers, the fMRI signal was greater after exposure to smoking-related images than after exposure to neutral images in mesolimbic dopamine reward circuits known to be activated by addictive drugs (right posterior amygdala, posterior hippocampus, ventral tegmental area, and medial thalamus) as well as in areas related to visuospatial attention (bilateral prefrontal and parietal cortex and right fusiform gyrus). In nonsmokers, no significant differences in fMRI signal following exposure to smoking-related and neutral images were detected. In most regions studied, both subject groups showed greater activation following presentation of rare target images than after exposure to neutral images. CONCLUSIONS: In nicotine-deprived smokers, both reward and attention circuits were activated by exposure to smoking-related images. Smoking cues are processed like rare targets in that they activate attentional regions. These cues are also processed like addictive drugs in that they activate mesolimbic reward regions.

Magnetic resonance imaging of graded skeletal muscle injury in live rats.

INTRODUCTION: Increasing number of stretch-shortening contractions (SSCs) results in increased muscle injury. METHODS: Fischer Hybrid rats were acutely exposed to an increasing number of SSCs in vivo using a custom-designed dynamometer. Magnetic resonance imaging (MRI) imaging was conducted 72 hours after exposure when rats were infused with Prohance and imaged using a 7T rodent MRI system (GE Epic 12.0). Images were acquired in the transverse plane with typically 60 total slices acquired covering the entire length of the hind legs. Rats were euthanized after MRI, the lower limbs removed, and tibialis anterior muscles were prepared for histology and quantified stereology. RESULTS: Stereological analyses showed myofiber degeneration, and cellular infiltrates significantly increased following 70 and 150 SSC exposure compared to controls. MRI images revealed that the percent affected area significantly increased with exposure in all SSC groups in a graded fashion. Signal intensity also significantly increased with increasing SSC repetitions. DISCUSSION: These results suggest that contrast-enhanced MRI has the sensitivity to differentiate specific degrees of skeletal muscle strain injury, and imaging data are specifically representative of cellular histopathology quantified via stereological analyses.

Localization of Metal Electrodes in the Intact Rat Brain Using Registration of 3D Microcomputed Tomography Images to a Magnetic Resonance Histology Atlas.

Simultaneous neural recordings taken from multiple areas of the rodent brain are garnering growing interest due to the insight they can provide about spatially distributed neural circuitry. The promise of such recordings has inspired great progress in methods for surgically implanting large numbers of metal electrodes into intact rodent brains. However, methods for localizing the precise location of these electrodes have remained severely lacking. Traditional histological techniques that require slicing and staining of physical brain tissue are cumbersome, and become increasingly impractical as the number of implanted electrodes increases. Here we solve these problems by describing a method that registers 3-D computerized tomography (CT) images of intact rat brains implanted with metal electrode bundles to a Magnetic Resonance Imaging Histology (MRH) Atlas. Our method allows accurate visualization of each electrode bundle's trajectory and location without removing the electrodes from the brain or surgically implanting external markers. In addition, unlike physical brain slices, once the 3D images of the electrode bundles and the MRH atlas are registered, it is possible to verify electrode placements from many angles by "re-slicing" the images along different planes of view. Further, our method can be fully automated and easily scaled to applications with large numbers of specimens. Our digital imaging approach to efficiently localizing metal electrodes offers a substantial addition to currently available methods, which, in turn, may help accelerate the rate at which insights are gleaned from rodent network neuroscience.

Quantitative mapping of trimethyltin injury in the rat brain using magnetic resonance histology.

The growing exposure to chemicals in our environment and the increasing concern over their impact on health have elevated the need for new methods for surveying the detrimental effects of these compounds. Today's gold standard for assessing the effects of toxicants on the brain is based on hematoxylin and eosin (H&E)-stained histology, sometimes accompanied by special stains or immunohistochemistry for neural processes and myelin. This approach is time-consuming and is usually limited to a fraction of the total brain volume. We demonstrate that magnetic resonance histology (MRH) can be used for quantitatively assessing the effects of central nervous system toxicants in rat models. We show that subtle and sparse changes to brain structure can be detected using magnetic resonance histology, and correspond to some of the locations in which lesions are found by traditional pathological examination. We report for the first time diffusion tensor image-based detection of changes in white matter regions, including fimbria and corpus callosum, in the brains of rats exposed to 8 mg/kg and 12 mg/kg trimethyltin. Besides detecting brain-wide changes, magnetic resonance histology provides a quantitative assessment of dose-dependent effects. These effects can be found in different magnetic resonance contrast mechanisms, providing multivariate biomarkers for the same spatial location. In this study, deformation-based morphometry detected areas where previous studies have detected cell loss, while voxel-wise analyses of diffusion tensor parameters revealed microstructural changes due to such things as cellular swelling, apoptosis, and inflammation. Magnetic resonance histology brings a valuable addition to pathology with the ability to generate brain-wide quantitative parametric maps for markers of toxic insults in the rodent brain.