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 199 records · Page 11Linked to original sources
PubMed1995

Artificial neural networks for recognition of electrocardiographic lead reversal.

Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm lead reversal. Neural networks were trained to detect ECGs with right/left arm lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm lead reversals being detected by networks but not by conventional interpretation programs.

Algorithms↗
PubMed1994

Application of a bi-directional associative memory (BAM) network in computer assisted learning in chemistry.

A computer assisted learning software based on a bi-directional associative memory (BAM) network was developed. The software was implemented to assist students in associating the names of the elements in the periodic table with their chemical symbols. The use of the BAM facilitates the analysis and interpretation of students' responses. The software package can be modified easily as an educational tool for other disciplines.

Algorithms↗
PubMed1997

Evaluation of simplified compartmental models of reconstructed neocortical neurons for use in large-scale simulations of biological neural networks.

The electrotonic properties of the complex arborizations of neurons can be simulated by creating compartmental models based on the morphology of real neurons. These models can be very detailed with thousands of individual compartments and active channels. Large numbers of these models can be linked together into biologically realistic, large-scale neural networks with which to obtain a better understanding of the interactions among real neurons. However, the use of detailed compartmental models in such large networks is hindered by long computation times. Methods exist to reduce the complex morphology of detailed compartmental models to simpler reconstructions that retain many of the electrotonic properties of the original model yet are computationally efficient. However, little work exists that evaluates the limitations and performance of such reduced models with realistic active conductances modeled in both the soma and the dendrites to ensure that they are appropriate for use in biologically realistic network models. We have created detailed and reduced models of reconstructed dye-filled neurons from rat somatosensory neocortex and evaluated the ability of the reduced models to faithfully reproduce the input-output functions of the more detailed models. We find that the reduced models are not capable of perfectly reproducing the exact output of the detailed models using identical parameters. However, if the parameters are adjusted the reduced models are certainly capable of providing input-output patterns that are well within an acceptable range of known neural activity. The limitations and the benefits of such models are discussed.

Animals↗
PubMed1993

Prediction of protein secondary structure at better than 70% accuracy.

We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70.8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70.2%. The prediction is well balanced between alpha-helix, beta-strand and loop: 65% of the observed strand residues are predicted correctly. The accuracy in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchers via an electronic mail server.

Mathematical Computing↗
PubMed1997

Analyses on the temporal patterns of spikes of auditory neurons of the macaque monkey by means of an artificial neural network and tree-based models.

The time scale over which information in the primary auditory cortex is processed was estimated. An artificial neural network was used to learn the temporal patterns of spikes. After learning, test patterns were input to the network. Comparison of the accuracy of the network with that of the maximum likelihood function computed from the spike count reveals that the temporal patterns of spikes are closely related to stimulus discrimination. Next, a tree-based model from a subset of the spike trains with a fixed time resolution was constructed and validated the model with another. By repeating this for different bin widths, it was found that there are no simple models for the time bin width larger than 50 ms. This indicates that the time scale in the auditory cortex is not larger than 50 ms.

Animals↗
PubMed1996

Web interface for the Heart Disease Program.

The task of making a large complex diagnostic program available to a broad audience of physicians has become more feasible with the ubiquitous accessibility of the client-server architecture of the World Wide Web. This paper describes the design and implementation of a Web interface for the Heart Disease Program (HDP). The client-server architecture imposes a number of requirements on the program. The graphical capabilities of the Web enable a number of enhancements to the program but also cause some limitations. Our initial experience with physicians using the HDP through the Web interface has been positive and we are now conducting an evaluation of the HDP using this form of access.

Computer Communication Networks↗
PubMed1994

Energy minimization method using automata network for sequence and side-chain conformation prediction from given backbone geometry.

Globular proteins have high packing densities as a result of residue side chains in the core achieving a tight, complementary packing. The internal packing is considered the main determinant of native protein structure. From that point of view, we present here a method of energy minimization using an automata network to predict a set of amino acid sequences and their side-chain conformations from a desired backbone geometry for de novo design of proteins. Using discrete side-chain conformations, that is, rotamers, the sequence generation problem from a given backbone geometry becomes one of combinatorial problems. We focused on the residues composing the interior core region and predicted a set of amino acid sequences and their side-chain conformations only from a given backbone geometry. The kinds of residues were restricted to six hydrophobic amino acids (Ala, Ile, Met, Leu, Phe, and Val) because the core regions are almost always composed of hydrophobic residues. The obtained sequences were well packed as was the native sequence. The method can be used for automated sequence generation in the de novo design of proteins.

Bacterial Proteins↗
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↗
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↗
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↗
PubMed1997

Update in digital mammography.

Digital mammography is a rapidly developing technology that has great potential to improve upon and ultimately replace conventional film-screen mammography for the early detection of breast cancer. This article reviews current progress in digital mammographic systems, computer-aided diagnostic programs, and artificial neural networks. Digital mammographic systems are currently in an investigational phase only. Large-scale clinical trials are needed in all areas of digital mammography before this exciting new technology can be implemented outside of research centers.

Artificial Intelligence↗