PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Neural Networks, Computer”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40Linked to original sources
PubMed1997

Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures.

In vivo intracellular recording from cat thalamus and cortex was performed during spontaneous spike-wave seizures characterized by synchronously firing cortical neurons correlated with the electroencephalogram. During these seizures, thalamic reticular (RE) neurons discharged with long spike bursts riding on a depolarization, whereas thalamocortical (TC) neurons were either entrained into the seizures (40%) or were quiescent (60%). During quiescence, TC neurons showed phasic inhibitory postsynaptic potentials (IPSPs) that coincided with paroxysmal depolarizing shifts in the simultaneously recorded cortical neuron. Computer simulations of a reciprocally connected TC-RE pair showed two major modes of TC-RE interaction. In one mode, a mutual oscillation involved direct TC neuron excitation of the RE neuron leading to a burst that fed back an IPSP into the TC neuron, producing a low-threshold spike. In the other, quiescent mode, the TC neuron was subject to stronger coalescing IPSPs. Simulated cortical stimulation could trigger a transition between the two modes. This transition could go in either direction and was dependent on the precise timing of the input. The transition did not always follow the stimulation immediately. A larger, multicolumnar simulation was set up to assess the role of the TC-RE pair in the context of extensive divergence and convergence. The amount of TC neuron spiking generally correlated with the strength of total inhibitory input, but large variations in the amount of spiking could be seen. Evidence for mutual oscillation could be demonstrated by comparing TC neuron firing with that in reciprocally connected RE neurons. An additional mechanism for TC neuron quiescence was assessed with the use of a cooperative model of gamma-aminobutyric acid-B (GABA(B))-mediated responses. With this model, RE neurons receiving repeated strong excitatory input produced TC neuron quiescence due to burst-duration-associated augmentation of GABA(B) current. We predict the existence of spatial inhomogeneity in apparently generalized spike-wave seizures, involving a center-surround pattern. In the center, intense cortical and RE neuron activity would be associated with TC neuron quiescence. In the surround, less intense hyperpolarization of TC neurons would allow low-threshold spikes to occur. This surround, an "epileptic penumbra," would be the forefront of the expanding epileptic wave during the process of initial seizure generalization. Therapeutically, we would then predict that agents that reduce TC neuron activity would have a greater effect on seizure onset than on ongoing spike-wave seizures or other thalamic oscillations.

Action Potentials↗
PubMed1996

Determining and classifying the region of interest in ultrasonic images of the breast using neural networks.

This paper describes how ultrasonic images of the female breast have been processed and neural nets used to aid the identification of malignant and benign areas in them. The images are windowed, filtered and pre-processed into suitable patterns for processing by a neural net. Two networks are trained and used: one for malignant cases and the other for benign cases. These are used to make predictions of regions of interest which are presented as circles overlaid on the image. The system has been prototyped and tested and experts agreed well with the classification and localisation. The system is usually weak when the evidence on the image is considered weak by the expert. It is concluded that the system is promising and should be developed further by providing more training to the network.

Breast Neoplasms↗
PubMed1999

Fast calculation of short-term depressing synaptic conductances.

An efficient implementation of synaptic transmission models in realistic network simulations is an important theme of computational neuroscience. The amount of CPU time required to simulate synaptic interactions can increase as the square of the number of units of such networks, depending on the connectivity convergence. As a consequence, any realistic description of synaptic phenomena, incorporating biophysical details, is computationally highly demanding. We present a consolidating algorithm based on a biophysical extended model of ligand-gated postsynaptic channels, describing short-term plasticity such as synaptic depression. The considerable speed-up of simulation times makes this algorithm suitable for investigating emergent collective effects of short-term depression in large-scale networks of model neurons.

Algorithms↗
PubMed1992

Visuomotor transformations underlying arm movements toward visual targets: a neural network model of cerebral cortical operations.

We propose a biologically realistic neural network that computes coordinate transformations for the command of arm reaching movements in 3-D space. This model is consistent with anatomical and physiological data on the cortical areas involved in the command of these movements. Studies of the neuronal activity in the motor (Georgopoulos et al., 1986; Schwartz et al., 1988; Caminiti et al., 1990a) and premotor (Caminiti et al., 1990b, 1991) cortices of behaving monkeys have shown that the activity of individual arm-related neurons is broadly tuned around a preferred direction of movements in 3-D space. Recent data demonstrate that in both frontal areas (Caminiti et al., 1990a,b, 1991) these cell preferred directions rotate with the initial position of the arm. Furthermore, the rotation of the population of preferred directions precisely corresponds to the rotation of the arm in space. The neural network model computes the motor command by combining the visual information about movement trajectory with the kinesthetic information concerning the orientation of the arm in space. The appropriate combination, learned by the network from spontaneous movement, can be approximated by a bilinear operation that can be interpreted as a projection of the visual information on a reference frame that rotates with the arm. This bilinear combination implies that neural circuits converging on a single neuron in the motor and premotor cortices can learn and generalize the appropriate command in a 2-D subspace but not in the whole 3-D space. However, the uniform distribution of cell preferred directions in these frontal areas can explain the computation of the correct solution by a population of cortical neurons. The model is consistent with the existing neurophysiological data and predicts how visual and somatic information can be combined in the different processing steps of the visuomotor transformation subserving visual reaching.

Animals↗
PubMedDate not supplied

Mining protein data from two-dimensional gels: tools for systematic post-planned analyses.

There is a considerable need to develop comprehensive, systematic mechanisms to analyze the vast number of proteins that orchestrate various cellular functions and to identify proteins associated with disease or that are affected by pharmacological agents. Two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) continues to be relied upon to analyze protein constituents of cells and tissues. We have developed a Laboratory Information Processing System (LIPS) as a computer-based tool for capturing quantitative and qualitative changes in thousands of proteins detected in 2-D gels of various types. Protein databases have been developed to serve as a repository for data processing of the basic and derived data and of findings derived from different studies. There have been remarkable advances both in database technology as well as in the computer hardware that have benefited our effort at mining protein data from 2-D gels. We here review our current efforts aimed at improving the performance and features of our 2-D related protein databases, with particular emphasis on the tools we utilize for database mining via a systematic analysis of information known as post-planned analysis.

Acrylic Resins↗
PubMed1992

Molecular computing for edge-enhanced laser imaging.

In order to illustrate the self-assembly capability, we consider a laser imaging experiment on a wet film that is made of bacteriorhodopsin (BR) molecules suspended in a diffusion-limited viscous medium. BR wet film is similar to a wet photograph film but having a finer resolution and adaptive pixel locations due to laser-induced thermal diffusion. The synergism between thermal diffusion of BR molecules (induced externally by a write-laser) and molecular photochromism (generated internally by a read-laser) is exploited naturally for edge-enhanced image applications.

Bacteriorhodopsins↗
PubMed1995

Introduction to the Bioelectronic Devices Project in Japan.

The Bioelectronic Devices Project was organized in Japan as a 10-year national project and it has been working toward developing fundamental key technologies for designing and assembling innovative information-processing devices by realizing the excellent functions specifically found in molecular assemblies and information processing of living organisms. The project is now in the third year of the second phase (the 8th year of its 10-year duration) and researchers have been trying to elucidate the specific characters of the prototype devices. The outline of this challenging project is given with the latest experimental results.

Electron Transport↗
PubMed1995

The future of MEBC: panel discussion.

The expected developments in the not too distant future (5-10 years) of molecular electronics and biocomputing (MEBC) are discussed. In the short-term, the study of very specific basic phenomena is expected (e.g. conducting polymers, strange electronic states of insulating polymers, bacteriorhodopsin (BR), arrays of molecules, self-organization of biomaterials, very specific biological systems, quantum coherence in cytoskeletal microtubules, optoelectronic information storage, associative memories, pattern recognition, hierarchical nature of biological information). New application fields outside the range of conventional technology (e.g. randomized algorithms, optoelectronic devices, chemical and biosensors, as well as a certain extent of commercialisation) have also been predicted. In the long-term, the study and solution of much deeper (sometimes scientific fiction-like) problems were foreseen, such as the self-organization of biomaterials, artificial self-reproduction, implementation of artificial cell dynamic control structures based on molecular devices for medical and environmental applications and the construction of neuronal computers as aids to the human brain.

Electronics, Medical↗
PubMed2006

Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis.

Unilateral vocal fold paralysis (UVFP) is one of the most severe types of neurogenic laryngeal disorder in which the patients, due to their vocal cords malfunction, are confronted by some serious problems. As the effect of such pathologies would be significantly evident in the reduced quality and feature variation of dysphonic voices, this study is designed to scrutinize the piecewise variation of some specific types of these features, known as energy and entropy, all over the frequency range of pathological speech signals. In order to do so, the wavelet-packet coefficients, in five consecutive levels of decomposition, are used to extract the energy and entropy measures at different spectral sub-bands. As the decomposition procedure leads to a set of high-dimensional feature vectors, genetic algorithm is invoked to search for a group of optimal sub-band indexes for which the extracted features result in the highest recognition rate for pathological and normal subjects' classification. The results of our simulations, using support vector machine classifier, show that the highest recognition rate, for both optimized energy and entropy measures, is achieved at the fifth level of wavelet-packet decomposition. It is also found that entropy feature, with the highest recognition rate of 100% vs. 93.62% for energy, is more prominent in discriminating patients with UVFP from normal subjects. Therefore, entropy feature, in comparison with energy, demonstrates a more efficient description of such pathological voices and provides us a valuable tool for clinical diagnosis of unilateral laryngeal paralysis.

Adolescent↗
PubMedDate not supplied

A model of active visual search with object-based attention guiding scan paths.

When a monkey searches for a colour and orientation feature conjunction target, the scan path is guided to target coloured locations in preference to locations containing the target orientation [Vision Res. 38 (1998b) 1805]. An active vision model, using biased competition, is able to replicate this behaviour. As object-based attention develops in extrastriate cortex, featural information is passed to posterior parietal cortex (LIP), enabling it to represent behaviourally relevant locations [J. Neurophysiol. 76 (1996) 2841] and guide the scan path. Attention evolves from an early spatial effect to being object-based later in the response of the model neurons, as has been observed in monkey single cell recordings. This is the first model to reproduce these effects with temporal precision and is reported here at the systems level allowing the replication of psychophysical scan paths.

Animals↗
PubMed2005

Adaptive algorithms for first principal eigenvector computation.

The paper presents a unified framework to derive and analyze 10 different adaptive algorithms, some well-known, to compute the first principal eigenvector of the correlation matrix of a random vector sequence. Since adaptive principal eigenvector algorithms have originated from a diverse set of disciplines, including ad hoc methods, it is necessary to examine them in a unified framework. In a common framework consisting of five steps, we analyze the derivation, convergence, and rate results for many well-known algorithms as well as two new adaptive algorithms. In the process, we offer fresh perspectives on the known algorithms, and derive new results for others. The common framework also allows us to comparatively study the 10 algorithms. Finally, we show experimental results to support our analyses.

Algorithms↗
PubMed2006

Assessing self organizing maps via contiguity analysis.

Contiguity analysis is a straightforward generalization of linear discriminant analysis in which the partition of elements is replaced by a more general graph structure. Applied to the graph induced by a Self Organizing Map (SOM), contiguity analysis provides a set of linear projectors leading to a planar representation as close as possible to the SOM. As expected, such projectors may only concern local parts of the SOMs. They allow us to visualize the shapes of the clusters (convex hulls of the projections of the elements belonging to a cluster) and the pattern of the elements within each cluster. In some contexts, it is possible to project the bootstrap replicates of the elements, and therefore to produce confidence areas for elements via a standard partial bootstrap procedure.

Algorithms↗
PubMed2000

Visualization of data.

Data visualization has developed in several directions: theoretical; methodological; and in new application areas. Advances include the development of a grammar of graphics, deeper understanding of human perception and implications for graphical layout, and better approaches to visualizing multi-dimensional data and large data sets. Gene expression is a notable new application area for visualization of large data sets.

Biotechnology↗
PubMed1998

A molar entropy model of age differences in spatial memory.

Two very-short-term-memory, spatial scanning aging experiments were conducted involving a graphics character as a target stimulus. On the probe portion of a trial, the stimulus was presented in the same position as it was on the target portion of the trial (i.e., a same trial) 50% of the time. However, on the remaining 50% of the trials, the probe stimulus was shifted (or transposed) 1, 2, or 3 positions to the right or left of the original presentation (target) position. In Experiment 1, exposure duration was manipulated. In Experiment 2, the number of potential target display positions was manipulated. For both experiments, older adults showed larger transposition distance effects than younger adults for errors. In the past (e.g., P.A. Allen, 1990, 1991), this effect has been attributed to higher levels of internal noise (entropy) in older than younger adults. This research provides converging operations to this contention by using statistical physics methods to rigorously compute the entropy in a molar neural network across age groups. After successfully fitting the statistical mechanics model to the data, the model is proved to have external validity by fitting a simplified version of it to an earlier spatial memory aging experiment reported by P. R. Bruce and J. F. Herman (1986). The results of both traditional reaction time and error rate analyses, as well as the entropy modeling analyses, indicated that older adults exhibited higher levels of entropy than did the younger adults and that this effect appeared to be generalized across processing stage.

Adolescent↗
PubMed2004

Biological applications of support vector machines.

One of the major tasks in bioinformatics is the classification and prediction of biological data. With the rapid increase in size of the biological databanks, it is essential to use computer programs to automate the classification process. At present, the computer programs that give the best prediction performance are support vector machines (SVMs). This is because SVMs are designed to maximise the margin to separate two classes so that the trained model generalises well on unseen data. Most other computer programs implement a classifier through the minimisation of error occurred in training, which leads to poorer generalisation. Because of this, SVMs have been widely applied to many areas of bioinformatics including protein function prediction, protease functional site recognition, transcription initiation site prediction and gene expression data classification. This paper will discuss the principles of SVMs and the applications of SVMs to the analysis of biological data, mainly protein and DNA sequences.

Algorithms↗