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PubMed2005

Chaotic neurodynamics for autonomous agents.

Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limit-cycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multilayer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

Action Potentials↗
PubMed2005

Auditory learning: a developmental method.

Motivated by the human autonomous development process from infancy to adulthood, we have built a robot that develops its cognitive and behavioral skills through real-time interactions with the environment. We call such a robot a developmental robot. In this paper, we present the theory and the architecture to implement a developmental robot and discuss the related techniques that address an array of challenging technical issues. As an application, experimental results on a real robot, self-organizing, autonomous, incremental learner (SAIL), are presented with emphasis on its audition perception and audition-related action generation. In particular, the SAIL robot conducts the auditory learning from unsegmented and unlabeled speech streams without any prior knowledge about the auditory signals, such as the designated language or the phoneme models. Neither available before learning starts are the actions that the robot is expected to perform. SAIL learns the auditory commands and the desired actions from physical contacts with the environment including the trainers.

Algorithms↗
PubMed2006

Links between PPCA and subspace methods for complete Gaussian density estimation.

High-dimensional density estimation is a fundamental problem in pattern recognition and machine learning areas. In this letter, we show that, for complete high-dimensional Gaussian density estimation, two widely used methods, probabilistic principal component analysis and a typical subspace method using eigenspace decomposition, actually give the same results. Additionally, we present a unified view from the aspect of robust estimation of the covariance matrix.

Algorithms↗
PubMed2006

Upport vector machines for nonlinear kernel ARMA system identification.

Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.

Algorithms↗
PubMed2006

Permutation coding technique for image recognition systems.

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Algorithms↗
PubMed2006

Neural-electronic inhibition simulated with a neuron model implemented in SPICE.

There have been numerous studies presented in the literature related to the simulation of the interaction between biological neurons and electronic devices. A complicating factor associated with these simulations is the algebraic complexity involved in implementation. This complication has impeded simulation of more involved neural-electronic circuitry and consequently has limited potential advancements in the integration of biological neurons with synthetic electronics. In this paper, we describe a modification to a previously proposed SPICE based Hodgkin-Huxley neuron model that demonstrates more physiologically relevant electrical behavior. We utilize this SPICE based neuron model in conjunction with an external circuit that allows for artificial selective inhibition of neural spiking. The neural firing control scheme proposed herein would allow for action potential frequency modulation of neural activity that, if developed further, could potentially be applied to suppress undesirable neural activity that manifests symptomatically as the tremors or seizures associated with specific pathologies of the nervous system.

Action Potentials↗
PubMedDate not supplied

From quantitative microscopy to automated image understanding.

Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, and computational processing of digitized images. Although many of the latest digital signal processing techniques have been successfully applied to compress, restore, and register digital microscope images, automated approaches for recognition and understanding of complex subcellular patterns in light microscope images have been far less widely used. We describe a systematic approach for interpreting protein subcellular distributions using various sets of subcellular location features (SLF), in combination with supervised classification and unsupervised clustering methods. These methods can handle complex patterns in digital microscope images, and the features can be applied for other purposes such as objectively choosing a representative image from a collection and performing statistical comparisons of image sets.

Algorithms↗
PubMed1998

Identification of lung regions in chest radiographs using Markov random field modeling.

The authors present an algorithm utilizing Markov random field modeling for identifying lung regions in a digitized chest radiograph (DCR). Let x represent the classifications of each pixel in a DCR as either lung or nonlung. We model x as a realization of a spatially varying Markov random field. This model is developed utilizing spatial and textural information extracted from samples of lung and nonlung region-types in a training set of DCRs. With this model, the technique of Iterated Conditional Modes is used to determine the optimal classification of each pixel in a DCR. The algorithm's ability to identify lung regions is evaluated on a testing set of DCRs. The algorithm performs well yielding a sensitivity of 90.7% +/- 4.4%, a specificity of 97.2% +/- 2.0%, and an accuracy of 94.8% +/- 1.6%. In an attempt to gain insight into the meaning and level of the algorithm's performance numbers, the results are compared to those of some easily implemented classification algorithms.

Algorithms↗
PubMed1998

Effects of wide band inhibitors in the dorsal cochlear nucleus. II. Model calculations of the responses to complex sounds.

In a previous modeling study of signal processing in the dorsal cochlear nucleus [Reed and Blum, J. Acoust. Soc. Am 96, 1442-1453 (1997)] it was shown that inclusion of a wideband inhibitor (WBI) greatly improved the fit between model response maps and the experimental response maps of type IV units to pure tones. In this study we examine the effect of the WBI on the responses to complex sound stimuli such as broadband noise (BBN), notch noise, noise bands, and band/notch combinations. A new and more realistic model for auditory nerve (AN) response in the presence of different levels of noise is used. It is shown that one can explain and understand the qualitative features of virtually all the published data on type II and type IV unit responses to BBN, notch noise and noise bands. The monotone decreasing response of the maximum firing rate of type II units to noise bands of increasing width that is observed experimentally occurs in the model due to the increasing inhibition of type II cells by the WBI. Similarly, the various nonmonotone patterns of maximum firing rate of type IV units to noise bands of increasing width is shown to arise from the complex and highly nonlinear effects of inhibition from the type II to type IV and the WBI to type IV cells and the nonlinear direct excitation from the AN to the type IV cells. A number of experiments using double notches, double noise bands, or notch-noise band pairs are suggested which, by comparison with model results, would allow one to infer probable connectional patterns between type II and type IV units and between the WBI units and the type IV units.

Acoustic Stimulation↗
PubMedDate not supplied

Automated diagnosis of data-model conflicts using metadata.

The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit.

Algorithms↗
PubMed2001

Probability density function learning by unsupervised neurons.

In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.

Algorithms↗
PubMedDate not supplied

Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies.

Since 1996, computer-aided diagnosis (CAD) schemes have been presented as interactive demonstrations on computer workstations at each scientific assembly of the Radiological Society of North America. The schemes involved (a) detection of pulmonary nodules, (b) temporal subtraction, (c) detection of interstitial lung disease, (d) differential diagnosis of interstitial lung disease, and (e) distinction between benign and malignant pulmonary nodules on chest radiographs. Large-scale observer tests were carried out to examine how radiologists can benefit from CAD systems. Observer performance was evaluated by analysis of receiver operating characteristic (ROC) curves. The statistical significance of the difference between the areas under the ROC curves without and with CAD was analyzed with the Student t test. In all of the tests, the diagnostic accuracy of the radiologists in total improved significantly when CAD was used. This result provides additional evidence that CAD has the potential to improve the performance of radiologists in their decision-making process in interpreting chest radiographs.

Diagnosis, Computer-Assisted↗
PubMedDate not supplied

Negative cervical smears before CIN 3/carcinoma. Reevaluation with the PAPNET Testing System.

OBJECTIVE: To test the effectiveness of the PAPNET testing system in identifying false negative smears, using archival cervical cytologic smears from women with histologically proven diagnoses of high grade lesions and carcinoma of the uterine cervix. STUDY DESIGN: Forty-six negative smears from women who developed a high grade cervical intraepithelial lesion (CIN 3) or carcinoma of the uterine cervix within three years were retrieved from the archives, plus 20 consecutive control smears for each case. The smears were analyzed with the PAPNET testing system, and the selected cells were reviewed by a cytotechnologist using a strict protocol. RESULTS: With the PAPNET testing system, 9 of 46 (20%) smears were positive. Seven were reclassified as low grade and two reclassified as high grade squamous intraepithelial lesion (SIL). One of the 31 initially positive smears in the control group of 920 smears was not recognized as such. In the control group of 889 negative smears, 14 newly identified positive cases (1.6%) were detected, all low grade SIL. CONCLUSION: The PAPNET testing system is a good tool for detecting false negative smears and, when used as an adjunct to conventional screening, can reduce the false negative rate.

Automation↗
PubMedDate not supplied

Quality assurance in cervical cytology screening. Comparison of rapid rescreening and the PAPNET Testing System.

OBJECTIVE: To assess the performance of the PAPNET Testing System and compare the sensitivity of this automated device with that of rapid rescreening. STUDY DESIGN: In this study, 1,000 cervical smears previously diagnosed by our laboratory as negative were seeded with 20 particularly "difficult" cases. We submitted this seeded set of smears for rapid rescreening and to PAPNET to determine cases that could be detected by rapid rescreening, PAPNET or both. RESULTS: Rapid rescreening detected 9 of the 20 cases (45%). The PAPNET system identified 19/20 (95%). This investigation found PAPNET rescreening to be more effective than rapid rescreening in detecting the seeded difficult cases. CONCLUSION: Use of the PAPNET Testing System in tandem with rapid rescreening can reduce the rate of false negatives in diagnostically difficult cervical cytologic smears.

Automation↗
PubMedDate not supplied

Consistency of a double PAPNET scan of cervical smears.

OBJECTIVE: To determine the reproducibility and accuracy of the PAPNET system in finding and presenting abnormal and atypical cells. STUDY DESIGN: To assess the potential variation of a double PAPNET scan on the same cervical smear, 516 cervical smears from women with abnormal histologic diagnoses were scanned twice via PAPNET and reviewed by two independent examiners in a double-blind trial. RESULTS: The false negative rate of 5.7% in conventional screening was reduced to 0.4% and 0.8%, respectively, by PAPNET testing. The resulting concurrence of the first and the second PAPNET reviews was 99.2%, indicating the accuracy, sensitivity and consistency of this supplemental test. CONCLUSION: The results obtained demonstrate the reproducibility and accuracy of the system in finding and presenting abnormal and atypical cells.

Automation↗
PubMed2004

Modeling mental navigation in scenes with multiple objects.

Various lines of evidence indicate that animals process spatial information regarding object locations differently from spatial information regarding environmental boundaries or landmarks. Following Wang and Spelke's (2002) observation that spatial updating of egocentric representations appears to lie at the heart of many navigational tasks in many species, including humans, we postulate a neural circuit that can support this computation in parietal cortex, assuming that egocentric representations of multiple objects can be maintained in prefrontal cortex in spatial working memory (not simulated here). Our method is a generalization of an earlier model by Droulez and Berthoz (1991), with extensions to support observer rotation. We can thereby simulate perspective transformation of working memory representations of object coordinates based on an egomotion signal presumed to be generated via mental navigation. This biologically plausible transformation would allow a subject to recall the locations of previously viewed objects from novel viewpoints reached via imagined, discontinuous, or disoriented displacement. Finally, we discuss how this model can account for a wide range of experimental findings regarding memory for object locations, and we present several predictions made by the model.

Animals↗
PubMed2006

Consistency of pseudolikelihood estimation of fully visible Boltzmann machines.

A Boltzmann machine is a classic model of neural computation, and a number of methods have been proposed for its estimation. Most methods are plagued by either very slow convergence or asymptotic bias in the resulting estimates. Here we consider estimation in the basic case of fully visible Boltzmann machines. We show that the old principle of pseudolikelihood estimation provides an estimator that is computationally very simple yet statistically consistent.

Algorithms↗