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At least 1,711 records · Page 95Linked to original sources
PubMed2004

Artificial neural network analysis of gustatory responses in the thalamic taste relay of the rat.

We used an artificial neural network (ANN) as a model for analyzing single-neuron responses from the thalamic taste relay of rats. The network consisted of: (1) a layer of 44 input units, representing the responses of the 44 thalamic taste cells; (2) a layer of hidden units of varying numbers; and (3) a layer of four output units. We used the back-propagation algorithm to train the output units to discriminate among tastants based on inputs from the thalamic neurons. As the network became fully trained, we found that: (1) only two hidden units were necessary to provide nearly the full discriminative capacity of the network; (2) the loss of even a few of the input units that had the highest impact on hidden units caused a drastic reduction of discriminative power, implying that not all neurons contribute equally to the neural code; and (3) adding a temporal component to the input, by representing each 100-ms time bin as a separate input unit, increased the accuracy with which output units were able to identify tastants. We used data from behavioral discrimination tasks as a measure of the capacity of the network to identify stimuli correctly. A network with two hidden units was about as effective as an across-pattern analysis in accounting for the rat's discriminative ability.

Animals↗
PubMed2004

Non-linear filtering of ultrasonic signals using neural networks.

Structure noise from inhomogeneous micro-structures makes the detection of flaws present in highly scattering materials difficult. Several techniques have been applied to improve the signal-to-noise ratio (SNR) in order to make flaw detection easier. Linear filtering does not provide good results because both structure noise and flaw signal concentrate energy in the same frequency band. Non-linear filtering can be used to reduce the structure noise of ultrasonic signals. Therefore, neural networks are applied in this work for this purpose. In order to use neural networks for non-linear filtering, dynamic structures must be applied. The easiest way to implement a neural network with the capability of processing temporal patterns is to consider them spatial ones, applying the signal into a tapped delay line of finite extension, that is the input of a static neural network (for example, a multi-layer perceptron). In this work, a dynamic neural network has been built to filter ultrasonic signals with structure noise, and has been trained with the real-time back-propagation algorithm, using as inputs 3000 synthetic ultrasonic signals of 896 samples each. Target signals for training are the same as the ones used as inputs but without noise. The neural network is trained in order to generate as output the target signal when the noisy input one is applied. For testing the performance of the non-linear filter, a new set of 500 noisy signals has been used. The SNR improvement is about 6 dB average. The results show that this non-linear filtering method is quite useful as pre-processing stage in flaw detection systems.

Algorithms↗
PubMed2005

Computational representation of developmental genetic regulatory networks.

Developmental genetic regulatory networks (GRNs) have unique architectural characteristics. They are typically large-scale, multi-layered, and organized in a nested, modular hierarchy of regulatory network kernels, function-specific building blocks, and structural gene batteries. They are also inherently multicellular and involve changing topological relationships among a growing number of cells. Reconstruction of developmental GRNs requires unique computational tools that support the above representational requirements. In addition, we argue that DNA-centered network modeling, separate descriptions of network organization and network behavior, and support for network documentation and annotation are essential requirements for computational modeling of developmental GRNs. Based on these observations, we have developed a freely available, platform-independent, open source software package (BioTapestry) which supports both the process of model construction and also model visualization, analysis, documentation, and dissemination. We provide an overview of the main features of BioTapestry. The BioTapestry software and additional documents are available from http://www.biotapestry.org. We recommend BioTapestry as the substrate for further co-development for and by the developmental biology community.

Computer Simulation↗
PubMed1996

Neural networks to identify glaucoma with structural and functional measurements.

PURPOSE: Neural networks can recognize patterns and classify complex variables. We assessed the ability of neural networks to discriminate between normal and glaucomatous eyes by using structural and functional measurements. METHODS: Several neural network algorithms were tested with a database of 185 eyes of patients with early glaucomatous visual field loss (average mean defect, 4.5 dB) and 54 eyes of age-matched normal control subjects. The information used included automated visual field indices (mean defect, corrected loss variance, and short-term fluctuation) and structural data (cup/disk ratio, rim area, cup volume, and nerve fiber layer height) from computerized image analysis. RESULTS: A back propagation network with two intermediate layers assigned an estimated probability of being glaucomatous to each eye and correctly identified 88% of all eyes with 90% sensitivity and 84% specificity. The same neural network trained with only structural data correctly identified 80% of the eyes with 87% sensitivity and 56% specificity, and when trained with functional data only, it correctly identified 84% of the eyes with 84% sensitivity and 86% specificity. CONCLUSION: Analysis of several optic nerve and visual field variables by neural networks can help identify early glaucomatous damage and assign an estimated probability that early damage is present in individual patients.

Algorithms↗
PubMed1999

Classification of ion mobility spectra by functional groups using neural networks.

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Alcohols↗
PubMed1997

A study of learning splice sites of DNA sequence by neural networks.

To predict of splice sites in DNA sequence, we developed a neural network system with back propagation. This system has a flexible network definition language which can describe any network structure. Three types of neural network were defined using the system for the prediction of splice sites. The neural networks are trained by the arrangements of bases around the splice sites of DNA sequences. The results of simulation showed the excellent ability of the neural networks to predict splice sites by applying and testing the arrangements of DNA sequences. This system also were used to predict the effects of point mutations on the splicing of the IX factor gene which may cause hereditary disease.

Algorithms↗
PubMed1997

Sequential versus standard neural networks for pattern recognition: an example using the domain of coronary heart disease.

The goal of this study was to compare standard and sequential neural network models for recognition of patterns of disease progression. Medical researchers who perform prognostic modeling usually oversimplify the problem by choosing a single point in time to predict outcomes (e.g. death in 5 years). This approach not only fails to differentiate patterns of disease progression, but also wastes important information that is usually available in time-oriented research data bases. The adequate use of sequential neural networks can improve the performance of prognostic systems if the interdependencies among prognoses at different intervals of time are explicitly modeled. In such models, predictions for a certain interval of time (e.g. death within 1 year) are influenced by predictions made for other intervals, and prognostic survival curves that provide consistent estimates for several points in time can be produced. We developed a system of neural network models that makes use of time-oriented data to predict development of coronary heart disease (CHD), using a set of 2594 patients. The output of the neural network system was a prognostic curve representing survival without CHD, and the inputs were the values of demographic, clinical, and laboratory variables. The system of neural networks was trained by backpropagation and its results were evaluated in test sets of previously unseen cases. We showed that, by explicitly modeling time in the neural network architecture, the performance of the prognostic index, measured by the area under the receiver operating characteristic (ROC) curve, was significantly improved (p < 0.05).

Adult↗
PubMed1993

A neural network to analyze fertility data.

OBJECTIVE: To program an artificial intelligence system, a neural network, and use it to predict results of sperm penetration in bovine cervical mucus (Penetrak assay; Serono Laboratories, Norwell, MA) and zona-free hamster egg penetration from the semen analysis. DESIGN: Results of 139 Penetrak assays, 1,416 zona-free hamster egg penetration assays, and the corresponding semen analyses were retrospectively analyzed by an artificial neural network. MAIN OUTCOME MEASURES: Classification errors of the neural network were compared with those of linear and quadratic discriminant function analyses. RESULTS: Data were separated into training and test sets. For the Penetrak result, linear and quadratic discriminant function analysis correctly predicted 58% and 74% of the training set results and only 64.1% and 69.2% of the test data, respectively. The neural network correctly predicted 92% of training set results and 80% of test set results. For the zona-free hamster egg penetration assay outcome, linear and quadratic discriminant function analysis correctly classified 66.3% and 46.0% of the training set and 64.9% and 44.7% of the test set, respectively. The neural network correctly classified 75.7% of the training data and 67.8% of the test data. CONCLUSIONS: Using the semen analysis, the neural network correctly classified 67.8% of zona-free hamster egg penetration assay results and 80% of Penetrak results it had not encountered previously, suggesting that this method of data analysis may be successfully employed to predict fertility potential.

Animals↗
PubMed2002

Optimization of artificial neural networks used for retention modelling in ion chromatography.

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.

Chromatography, Liquid↗
PubMed1997

Predicting the toxicity of complex mixtures using artificial neural networks.

Industrial and municipal wastewaters constitute major sources of contamination of the aquatic compartment and represent a threat to aquatic life. Artificial neural networks based on three different learning paradigms were studied as a means of predicting acute toxicity to trout (5 days exposure to wastewaters) using input data from two simple microbiotests requiring only 5 or 15 min of incubation. These microbiotests were 1) the chemoluminescent peroxidase (Cl-Per) assay, which can detect radical scavengers and enzyme-inhibiting substances, and 2) the luminescent bacteria toxicity test (Microtox), in which reduction of light emission by bacteria during exposure is taken as a measure of toxicity. The responses obtained with the trout bioassay, the Cl-Per and the Microtox test were analyzed through statistical correlation (Pearson product-moment correlation), unsupervised learning by a self-organizing network, and assisted learning by the backpropagation and the Boltzmann machine (probabilistic) paradigms. No significant correlation (p < 0.05) was found between the responses obtained with either the Cl-Per assay (p = 0.121) or the Microtox (p = 0.061) microbiotest and those resulting from the trout bioassay. The self-organizing network was able to identify by itself a maximum of five classes that were more or less relevant for predicting toxicity to fish: class 1 contained 2 samples that were toxic to fish, class 2 contained 2/3 samples that were toxic, class 3 showed 6/8 samples that were non toxic, class 4 contained 5/6 samples that were non-toxic and class 5 comprised one sample that was toxic. Supervised learning with backpropagation analysis yielded two kinds of networks that hold potential. The first one was able to predict the actual toxic wastewater concentration with an overall performance of 65% when fed fresh data, while the second one, which was designed to differentiate between toxic and non-toxic effluents, exhibited a much better performance (90%). However, the probabilistic network also proved to be a very good predictive model for toxicity to fish, with an overall performance of 90%. Although more data are needed, the network based on the backpropagation paradigm seems to be a better predictor or classifier of trout toxicity when used with the Cl-Per and the Microtox microbiotests.

Algorithms↗
PubMed1992

Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction.

STUDY OBJECTIVE: To determine which clinical variables drive the output of an artificial neural network trained to identify the presence of myocardial infarction. DESIGN: Partial output analysis. SETTING: Tertiary university teaching center. PARTICIPANTS: Seven hundred six patients more than 18 years old presenting with anterior chest pain. MEASUREMENTS: Differential network output analysis. MAIN RESULTS: A methodology was developed as the first step in measuring the impact input clinical variables have on the output (diagnosis) of an artificial neural network trained to identify the presence of acute myocardial infarction. The methodology revealed that the network used the presence of ECG findings, as well as the presence of rales, syncope, jugular venous distension, response to trinitroglycerin, and nausea and vomiting, as major predictive sources. Although this first-step analysis studied individual variables, it must be stated that the network comes to clinical closure based on the settings of all variables in a pattern and that the impact of a single variable cannot be taken out of the context of a pattern. CONCLUSION: An artificial neural network trained to recognize the presence of myocardial infarction appears to place diagnostic importance on clinical variables that have not been shown previously to be highly predictive for infarction.

Adolescent↗
PubMed1999

novel Award Third Prize Paper. Assessment of the horizontal,fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks.

OBJECTIVE: In this study it was investigated whether an artificial neural network can be used to determine the horizontal, fore-aft component of the ground reaction force from insole pressure patterns. DESIGN: An artificial neural network was applied to map insole pressures and ground reaction forces. METHOD: To train an artificial neural network insole pressure patterns and ground reaction force data were simultaneously determined for a wide range of different speeds (0.9-2.3 m s(-1)) for five subjects. Both intrasubject and intersubject generalizability were evaluated. RESULTS: At the intrasubject level generalizability was good when the speed for which the force was to be predicted was within the range of speeds from which data were used to train the network. Besides in some cases, generalizability to a condition outside the range of training conditions could be demonstrated. At the intersubject level the quality of generalization differed widely over subjects, from poor to good. CONCLUSIONS: It was found that an artificial neural network is able to map the relationship between insole pressure patterns and the fore-aft component of the ground reaction force. RELEVANCE: Good intrasubject generalization of 'knowledge' obtained by an artificial neural network will allow the assessment of the fore-aft component of ground reaction force in condition that cannot be evaluated with force plates, e.g. activities of daily living or real sport situations. Additionally, intersubject generalization will allow shear-force recordings in subjects that are not able to complete a great number of runs to acquire enough force-plate hits.

Biomechanical Phenomena↗
PubMed2000

Potential of the back propagation neural network in the assessment of gait patterns in ankle arthrodesis.

OBJECTIVE: The purpose of this study was to recognize gait pattern in ankle arthrodesis by using a neural network trained with time domain input and compare the performance of the neural network with the statistical method. DESIGN: Three-layered feed-forward back propagation neural network and a statistical method were used to classify gait patterns of patients with ankle arthrodesis and normal subjects. BACKGROUND: Although backpropagation neural networks are very efficient in many pattern recognition tasks, they have not been used for gait pattern recognition of ankle arthrodesis. METHODS: A total of eighteen parameters measured by force platforms, including nine force parameters and their chronologic incidence of occurrence, were used to classify gait patterns. RESULTS: The results showed that the neural network model was able to classify subjects with recognition rates up to 95.8%. In contrast, the statistical method was only able to classify the subjects with recognition rates of 91.5%. CONCLUSIONS: The backpropagation neural network method has better accuracy than the statistical method in discriminating subjects and the time domain features carry important prognostic information. RELEVANCE: It is important to be able to quantify the changes in gait pattern after arthrodesis to understand the clinical implications of arthrodesis.

Adolescent↗
PubMed2001

Poor people, poor places, and poor health: the mediating role of social networks and social capital.

This paper explores the dynamics between poverty and exclusion; neighbourhood, and health and well being by considering the role of social networks and social capital in the social processes involved. It is based on qualitative research taking two deprived areas as exemplary case studies, and involving depth interviews with residents. Neighbourhood influences on networks and social capital were explored, network typologies developed reflecting structural and cultural aspects of individual's networks, and pathways implicated in health effects considered. The complexity of social capital is addressed. The role of three factors in influencing social networks and social capital are demonstrated: neighbourhood characteristics and perceptions; poverty and social exclusion, and social consciousness. Perceptions of inequality could be a source of social capital as well as demoralisation. Different network structures-dense and weak, homogeneous and heterogeneous- were involved in the creation of social capital and had implications for well being. Coping, enjoyment of life and hope are identified as benefits. Although participation in organisations was confirmed as beneficial, it is suggested that today's heterogeneous neighbourhoods also require regenerated local work opportunities to develop bridging ties necessary for the genesis of inclusive social capital and better health. Despite the capacity of social capital to buffer its harsher effects, the concept is not wholly adequate for explaining the deleterious effects of poverty on health and well being.

Adaptation, Psychological↗
PubMedDate not supplied

Social network type and health status in a national sample of elderly Israelis.

A typology of social support networks was examined in relation to five health measures in a national probability sample of Israelis aged 60 and over, using multiple classification analysis (N = 4214). The procedure revealed that the more resourceful diversified and friend and neighbor network types were consistently associated with better scores on measures of basic and instrumental activities of daily living, incontinence, vision and self-rated health, even when controlling for respondents' age, sex and education. The religious family network type, also endowed with considerable support potential, tended to correlate with lower health scores. The narrow family focused network had average health ratings or less, and a moderate support capability. The least resourceful network type, the attenuated network, was most frequently associated with poor health.

Aged↗
PubMed1999

Social network influences on reproductive health behaviors in urban northern Thailand.

Prevention approaches for reproductive health have evolved from an emphasis on individually focused models of behavior change to a recognition that risk reduction occurs within a context of social norms. Prevention programs can be improved by understanding how social structure influences sexual behavior and using that understanding to develop strategies for positive change. In a dynamic, urban context, communities are better conceptualized as informal networks of ties. These network structures may help to protect, or conversely, expose members to reproductive risk behaviors. Using data from a study of social and sexual networks conducted in northern Thailand, this article describes partner relations and social structure in the modern, urban context, and illustrates the links between individual, relational and structural properties and reproductive risk behaviors. Triangulation of ethnographic, survey and social network data collection and analytic tools provide an opportunity to interpret individual behaviors, meanings of relationships and structural properties of networks. Intervention approaches should build on existing networks, and address the complex meanings of romantic and sexual partnerships.

Community Networks↗
PubMed2003

Computer simulation of inhibition-dependent binding in a neural network.

Reverberating dynamics of neural network is modeled on PC in order to illustrate possible role of inhibition as binding controller in the network. The network is composed of binding neurons. In the binding neuron model [BioSystems 48 (1998) 263], the degree of temporal coherence between synaptic inputs is decisive for triggering, and slow inhibition is expressed in terms of the degree, which is necessary for triggering. Two learning mechanisms are implemented in the network, namely, adjusting synaptic strength and/or propagation delays. By means of forced playing of external pattern, the network is taught to support dynamics with disconnected and bound patterns of activity. By choosing either high, or low inhibition, one can switch between the disconnected and bound patterns, respectively. This is interpreted as inhibition-controlled binding in the network.

Computer Simulation↗
PubMed2001

Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks.

AIMS: To compare the prediction of mortality in individual extremely low birth weight (ELBW) neonates by regression analysis and by artificial neural networks. STUDY DESIGN: A database of 23 variables on 810 ELBW neonates admitted to a tertiary care center was divided into training, validation, and test sets. Logistic regression and neural network models were developed on the training set, validated, and outcome (mortality) predicted on the test set. Stepwise regression identified significant variables in the full set. Regression models and neural networks were then tested using data sets with only the identified significant variables, and then with variables excluded one at a time. RESULTS: The area under the curve (AUC) of receiver operating characteristic (ROC) curves for neural networks and regression was similar (AUC 0.87+/-0.03; p=0.31). Birthweight or gestational age and the 5-min Apgar score contributed most to AUC. CONCLUSIONS: Both neural networks and regression analysis predicted mortality with reasonable accuracy. For both models, analyzing selected variables was superior to full data set analysis. We speculate neural networks may not be superior to regression when no clear non-linear relationships exist.

Area Under Curve↗