Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Networks”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 1,747 records · Page 97Linked to original sources
PubMed1998

Frequency control in synchronized networks of inhibitory neurons.

We analyze the control of frequency for a synchronized inhibitory neuronal network. The analysis is done for a reduced membrane model with a biophysically based synaptic influence. We argue that such a reduced model can quantitatively capture the frequency behavior of a larger class of neuronal models. We show that in different parameter regimes, the network frequency depends in different ways on the intrinsic and synaptic time constants. Only in one portion of the parameter space, called phasic, is the network period proportional to the synaptic decay time. These results are discussed in connection with previous work of the authors, which showed that for mildly heterogeneous networks, the synchrony breaks down, but coherence is preserved much more for systems in the phasic regime than in the other regimes. These results imply that for mildly heterogeneous networks, the existence of a coherent rhythm implies a linear dependence of the network period on synaptic decay time and a much weaker dependence on the drive to the cells. We give experimental evidence for this conclusion.

Action Potentials↗
PubMed1998

Detection of lung injury with conventional and neural network-based analysis of continuous data.

OBJECTIVE: To test if analysis of pressure and flow waveform patterns with an artificial intelligence neural network could distinguish between normal and injured lungs. METHODS: Acute lung injury was induced in ten healthy anesthetized, mechanically ventilated dogs with repeated injections of oleic acid, until arterial blood oxyhemoglobin saturation reached 85% breathing room air. Airway pressure, esophageal pressure, airway flow, and arterial and mixed venous saturation signals were stored at 2 min intervals. Hemodynamic and blood gas data were collected every 10 min. Back-propagation neural networks were trained with normalized airway pressure and flow waveforms from normal and fully injured lungs. RESULTS: The networks scored lung injury on a continuous scale from +1 (normal) to -1 (injured). Network scores unequivocally distinguished between normal and fully injured lungs and suggested a gradual transition from normal to injury pattern. However, the response of the network was slow compared to compliance, resistance and venous admixture. CONCLUSIONS: Normal and fully injured lungs display distinct flow and pressure waveform patterns which are independent of changes in calculated pulmonary mechanics variables. These patterns can be recognized by a neural network. Further research is needed to determine the full potential of automated pattern recognition for lung monitoring.

Animals↗
PubMed1997

Performance of a neural-network-based determination of amino acid class and secondary structure from 1H-15N NMR data.

A neural network which can determine both amino acid class and secondary structure using NMR data from 15N-labeled proteins is described. We have included nitrogen chemical shifts, 3JHNH alpha coupling constants, alpha-proton chemical shifts, and side-chain proton chemical shifts as input to a three-layer feed-forward network. The network was trained with 456 spin systems from several proteins containing various types of secondary structure, and tested on human ubiquitin, which has no sequence homology with any of the proteins in the training set. A very limited set of data, representative of those from a TOCSY-HSQC and HNHA experiment, was used. Nevertheless, in 60% of the spin systems the correct amino acid class was among the top two choices given by the network, while in 96% of the spin systems the secondary structure was correctly identified. The performance of this network clearly shows the potential of the neural network algorithm in the automation of NMR spectral analysis.

Amino Acids↗
PubMed1998

An investigation of neural networks in thyroid function diagnosis.

We investigate the potential of artificial neural networks in diagnosing thyroid diseases. The robustness of neural networks with regard to sampling variations is examined using a cross-validation method. We illustrate the link between neural networks and traditional Bayesian classifiers. Neural networks can provide good estimates of posterior probabilities and hence can have better classification performance than traditional statistical methods such as logistic regression. The neural network models are further shown to be robust to sampling variations. It is demonstrated that for medical diagnosis problems where the data are often highly unbalanced, neural networks can be a promising classification method for practical use.

Bayes Theorem↗
PubMedDate not supplied

Clustering in small networks of excitatory neurons with heterogeneous coupling strengths.

Excitatory coupling with a slow rise time destabilizes synchrony between coupled neurons. Thus, the fully synchronous state is usually unstable in networks of excitatory neurons. Phase-clustered states, in which neurons are divided into multiple synchronized clusters, have also been found unstable in numerical studies of excitatory networks in the presence of noise. The question arises as to whether synchrony is possible in networks of neurons coupled through slow, excitatory synapses. In this paper, we show that robust, synchronous clustered states can occur in such networks. The effects of non-uniform distributions of coupling strengths are explored. Conditions for the existence and stability of clustered states are derived analytically. The analysis shows that a multi-cluster state can be stable in excitatory networks if the overall interactions between neurons in different clusters are stabilizing and strong enough to counter-act the destabilizing interactions between neurons within each cluster. When heterogeneity in the coupling strengths strengthens the stabilizing inter-cluster interactions and/or weakens the destabilizing in-cluster interactions, robust clustered states can occur in excitatory networks of all known model neurons. Numerical simulations were carried out to support the analytical results.

Cluster Analysis↗
PubMed2004

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks.

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.

Algorithms↗
PubMedDate not supplied

Extremely dilute modular neuronal networks: neocortical memory retrieval dynamics.

A model of columnar networks of neocortical association areas is studied. The neuronal network is composed of many Hebbian autoassociators, or modules, each of which interacts with a relatively small number of the others, randomly chosen. Any module encodes and stores a number of elementary percepts, or features. Memory items, or patterns, are peculiar combinations of features sparsely distributed over the multi-modular network. Any feature stored in any module can be involved in several of the stored patterns; feature-sharing is in fact source of local ambiguities and, consequently, a potential cause of erroneous memory retrieval spreading through the model network in pattern completion tasks. The memory retrieval dynamics of the large modular autoassociator is investigated by combining mathematical analysis and numerical simulations. An oscillatory retrieval process is proposed that is very efficient in overcoming feature-sharing drawbacks; it requires a mechanism that modulates the robustness of local attractors to noise, and neuronal activity sparseness such that quiescent and active modules are about equally noisy to any post-synaptic module.Moreover, it is shown that statistical correlation between 'kinds' of features across the set of memory patterns can be exploited to obtain a more efficient achievement of memory retrieval capabilities. It is also shown that some spots of the network cannot be reached by retrieval activity spread if they are not directly cued by the stimulus. The locations of these activity isles depend on the pattern to retrieve, while their extension only depends (in large networks) on statistics of inter-modular connections and stored patterns. The existence of activity isles determines an upper-bound to retrieval quality that does not depend on the specific retrieval dynamics adopted, nor on whether feature-sharing is permitted. The oscillatory retrieval process nearly saturates this bound.

Animals↗
PubMed2004

Classification of the frequency of carotid artery stenosis with MLP and RBF neural networks in patients with coroner artery disease.

For the classification of left and right Internal Carotid Arteries (ICA) stenosis, Doppler signals have been received from the patients with coroner arteries stenosis by using 6.2-8.4 MHz linear transducer. To be able to classify the data obtained from LICA and RICA in artificial intelligence, MLP and RBF neural networks were used. The number of obstructed veins from the coroner angiography, intimal thickness, and plaque formation from the power Doppler US and resistive index values were used as the input data for the neural networks. Our findings demonstrated that 87.5% correct classification rate was obtained from MLP neural network and 80% correct classification rate was obtained from RBF neural network. MLP neural network has classified more successfully when compared with RBF neural network.

Artificial Intelligence↗
PubMed1996

Queueing network modeling of elementary mental processes.

This article examines the use of reaction time (RT) to infer the possible configurations of mental systems and presents a class of queueing network models of elementary mental processes. The models consider the temporal issue of discrete versus continuous information transmission in conjunction with the architectural issue of serial versus network arrangement of mental processes. Five elementary but important types of queueing networks are described in detail with regard to their predictions for RT behavior, and they are used to re-examine existing models for psychological processes. As continuous-transmission networks in the general form, queueing network models include the existing discrete and continuous serial models and discrete network models as special cases, cover a broader range of temporal and architectural structures that mental processes might assume, and can be subjected to empirical tests.

Feedback↗
PubMed2005

Uncovering the overlapping community structure of complex networks in nature and society.

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.

Community Networks↗
PubMed2002

Neural networks--a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block.

The prognosis of acute myocardial infarction (AMI) improves by early revascularization. However the presence of left bundle branch block (LBBB) in the electrocardiogram (ECG) increases the difficulty in recognizing an AMI and different ECG criteria for the diagnosis of AMI have proved to be of limited value. The purpose of this study was to detect AMI in ECGs with LBBB using artificial neural networks and to compare the performance of the networks to that of six sets of conventional ECG criteria and two experienced cardiologists. A total of 518 ECGs, recorded at an emergency department, with a QRS duration > 120 ms and an LBBB configuration, were selected from the clinical ECG database. Of this sample 120 ECGs were recorded on patients with AMI, the remaining 398 ECGs being used as a control group. Artificial neural networks of feed-forward type were trained to classify the ECGs as AMI or not AMI. The neural network showed higher sensitivities than both the cardiologists and the criteria when compared at the same levels of specificity. The sensitivity of the neural network was 12% (P = 0.02) and 19% (P = 0.001) higher than that of the cardiologists. Artificial neural networks can be trained to detect AMI in ECGs with concomitant LBBB more effectively than conventional ECG criteria or experienced cardiologists.

Acute Disease↗
PubMed2004

Evolving complex dynamics in electronic models of genetic networks.

Ordinary differential equations are often used to model the dynamics and interactions in genetic networks. In one particularly simple class of models, the model genes control the production rates of products of other genes by a logical function, resulting in piecewise linear differential equations. In this article, we construct and analyze an electronic circuit that models this class of piecewise linear equations. This circuit combines CMOS logic and RC circuits to model the logical control of the increase and decay of protein concentrations in genetic networks. We use these electronic networks to study the evolution of limit cycle dynamics. By mutating the truth tables giving the logical functions for these networks, we evolve the networks to obtain limit cycle oscillations of desired period. We also investigate the fitness landscapes of our networks to determine the optimal mutation rate for evolution.

Biological Evolution↗
PubMed2003

Neural network approach to classify infective keratitis.

PURPOSE: Infective keratitis is a major sight-threatening condition in developing countries like India. An early diagnosis of infective keratitis is critical to its treatment. Epidemiological trends, morphological features of corneal ulceration and presence of other risk factors often dictate choice of initial treatment. This work assesses the usefulness of classification of infective keratitis by artificial neural network (ANN). METHODS: Forty input variables from each of the sixty-three known bacterial or fungal ulcers provided the basis for training a three layer feed-forward neural network. The trained neural network classified another set of forty-three corneal ulcers. RESULTS: Trained artificial neural network could classify correctly all sixty-three cornea ulcers in the training set. In the test set, the artificial neural network correctly classified 39 out of 43 cornea ulcers. Specificity for bacterial and fungal categories was 76.47% and 100% respectively. Accuracy of classification by neural network was 90.7% and compared significantly better than clinicians' prediction of 62.8% (p < 0.01). CONCLUSION: ANN has the potential to help clinicians classify corneal ulcers more accurately.

Corneal Ulcer↗
PubMed1997

Calliper randomization: an artificial neural network based analysis of E. coli ribosome binding sites.

An artificial neural network based approach has been used in analyzing the translation initiation region of E. coli. The approach is based on using a trained network capable of recognizing a particular region and presenting the network with randomized calliper inputs of the true sequence. The network responds with an error when the regions which have been the main source of knowledge are randomized. Analysis of the E. coli ribosome binding sites using this approach reveal that the initiation codon and the Shine/Dalgarno sequence which are known to be important for translation initiation are also important in imparting knowledge to the network. Further, selectively changing the usually occurring initiation codon AUG, to GUG, UUG and AUU, which occur less frequently, decreases the network performance in accordance with the frequency of their occurrence. This approach can be used as a general method to derive consensus.

Binding Sites↗
PubMed2006

Self-organizing path integration using a linked continuous attractor and competitive network: path integration of head direction.

A key issue is how networks in the brain learn to perform path integration, that is update a represented position using a velocity signal. Using head direction cells as an example, we show that a competitive network could self-organize to learn to respond to combinations of head direction and angular head rotation velocity. These combination cells can then be used to drive a continuous attractor network to the next head direction based on the incoming rotation signal. An associative synaptic modification rule with a short term memory trace enables preceding combination cell activity during training to be associated with the next position in the continuous attractor network. The network accounts for the presence of neurons found in the brain that respond to combinations of head direction and angular head rotation velocity. Analogous networks in the hippocampal system could self-organize to perform path integration of place and spatial view representations.

Action Potentials↗
PubMed2000

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data.

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.

Air Pollution, Indoor↗
PubMed2000

Prediction of programmed-temperature retention values of naphthas by artificial neural networks.

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.

Chemical Industry↗
PubMed1993

Neural network classification of mutagens using structural fragment data.

A neural network was applied to a large, structurally heterogeneous data set of mutagens and non-mutagens to investigate structure-property relationships. Substructural data comprising a total of 1280 fragments were used as inputs. The training of the back-propagation networks was directed by an algorithm which selected an optimal subset of fragments in order to maximize their discriminating power, and a good predictive network. The system comprised three programs: the first used a keyfile of 100 fragments to generate training and test files, the second was the network itself and a procedure for ranking the effectiveness of these fragments and the third randomly replaced the lowest fragments. This cycle was then repeated. After running on a 386/33 PC several networks produced approximately 11% failures in the test set and 6% in the training set. By simplifying the output of the hidden layer it was possible to describe the hidden layer states in terms of clusters of mutagens and non-mutagens. Some of these clusters were structurally homogeneous and contained known mutagenic and non-mutagenic structural classes. This analysis provided a useful means of demonstrating how the network was classifying the data.

Cluster Analysis↗