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PubMed1987

A model of neural network for spatiotemporal pattern recognition.

A model of neural network to recognize spatiotemporal patterns is presented. The network consists of two kinds of neural cells: P-cells and B-cells. A P-cell generates an impulse responding to more than one impulse and embodies two special functions: short term storage (STS) and heterosynaptic facilitation (HSF). A B-cell generates several impulses with high frequency as soon as it receives an impulse. In recognizing process, an impulse generated by a P-cell represents a recognition of stimulus pattern, and triggers the generation of impulses of a B-cell. Inhibitory impulses with high frequency generated by a B-cell reset the activities of all P-cells in the network. Two examples of spatiotemporal pattern recognition are presented. They are achieved by giving different values to the parameters of the network. In one example, the network recognizes both directional and non-directional patterns. The selectivities to directional and non-directional patterns are realized by only adjusting excitatory synaptic weights of P-cells. In the other example, the network recognizes time series of spatial patterns, where the lengths of the series are not necessarily the same and the transitional speeds of spatial patterns are not always the same. In both examples, the HSF signal controls the total activity of the network, which contributes to exact recognition and error recovery. In the latter example, it plays a role to trigger and execute the recognizing process. Finally, we discuss the correspondence between the model and physiological findings.

Brain↗
PubMed2001

Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks.

Current emphasis on efficient screening of novel therapeutic agents in toxicological studies has resulted in the evaluation of novel analytical technologies, including genomic (transcriptomic) and proteomic approaches. We have shown that high-resolution 1H NMR spectroscopy of biofluids and tissues coupled with appropriate chemometric analysis can also provide complementary data for use in in vivo toxicological screening of drugs. Metabonomics concerns the quantitative analysis of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification [Nicholson, J. K., Lindon, J. C., and Holmes, E. (1999) Xenobiotica 11, 1181-1189]. In this study, we have used 1H NMR spectroscopy to characterize the time-related changes in the urinary metabolite profiles of laboratory rats treated with 13 model toxins and drugs which predominantly target liver or kidney. These 1H NMR spectra were data-reduced and subsequently analyzed using a probabilistic neural network (PNN) approach. The methods encompassed a database of 1310 samples, of which 583 comprised a training set for the neural network, with the remaining 727 (independent cases) employed as a test set for validation. Using these techniques, the 13 classes of toxicity, together with the variations associated with strain, were distinguishable to >90%. Analysis of the 1H NMR spectral data by multilayer perceptron networks and principal components analysis gave a similar but less accurate classification than PNN analysis. This study has highlighted the value of probabilistic neural networks in developing accurate NMR-based metabonomic models for the prediction of xenobiotic-induced toxicity in experimental animals and indicates possible future uses in accelerated drug discovery programs. Furthermore, the sensitivity of this tool to strain differences may prove to be useful in investigating the genetic variation of metabolic responses and for assessing the validity of specific animal models.

Animals↗
PubMed1990

Experiments in artificial psychology: conditioning of asynchronous neural network models.

An asynchronous model for the dynamics of neural networks admits learning behaviors characteristic of classical and operant conditioning provided that appropriate plasticity algorithms are chosen. Stimulus generalization and discrimination can also be observed. Studies of such psychological phenomena are carried out by computer simulation of networks with designated sensory, association, and motor neurons, and the results are compared to those for live subjects. Various prescriptions for plasticity are investigated, including those corresponding to reward, punishment, and unlearning routines. These are characterized by their effect on network stability as quantified by a newly proposed stability measure.

Animals↗
PubMed1998

Extended Kalman Filter-Based Pruning Method for Recurrent Neural Networks.

Pruning is one of the effective techniques for improving the generalization error of neural networks. Existing pruning techniques are derived mainly from the viewpoint of energy minimization, which is commonly used in gradient-based learning methods. In recurrent networks, extended Kalman filter (EKF)-based training has been shown to be superior to gradient-based learning methods in terms of speed. This article explains a pruning procedure for recurrent neural networks using EKF training. The sensitivity of a posterior probability is used as a measure of the importance of a weight instead of error sensitivity since posterior probability density is readily obtained from this training method. The pruning procedure is tested using three problems: (1) the prediction of a simple linear time series, (2) the identification of a nonlinear system, and (3) the prediction of an exchange-rate time series. Simulation results demonstrate that the proposed pruning method is able to reduce the number of parameters and improve the generalization ability of a recurrent network.

Journal Article↗
PubMed2006

[The utility of artificial neural networks in the prediction of prostate cancer on transrectal biopsy].

OBJECTIVE: To determine whether the development of an artificial neural network (ANN) made up of clinical variables allows for the prediction of prostate biopsy (PB) outcome. MATERIALS AND METHODS: Patients (n=953) underwent PB at the Arquitecto Marcide Hospital in Ferrol (Spain), between january 2000 and june 2005. The variables studied were age, PSA, digital rectal examination (DRE) and prostate volume, data for all of which were available in 843 cases. In order to determine factors related to prostate cancer (PC) diagnosis, a logistic regression analysis and a feed-forward neural network were developed, including three hidden layer nodes and an output node, representing the probability of PC. Both models were constructed from a random sample of n=643 patients (derivation set). The predictive capacity was assessed with the remaining 200 patients (validation set), by means of ROC curves and the area under the curve (AUC). RESULTS: PC was detected in 500 (59.3%) cases. Adjusting for age, PSA, digital rectal examination and prostate volume, in a multivariate logistic regression model it was observed that all the variables were independent predictors of PC. The AUC were 0.693 for PSA, 0.707 for prostate volume, 0.815 for logistic regression and 0.819 for ANN. The predictive capacity of the ANN was significantly higher than that of the PSA (p=0.002) and prostate volume (p<0,001) and similar to that of logistic regression (p=0.760). CONCLUSIONS: The ANN shows a PC prediction capacity that is significantly higher than unimodal diagnosis methods, and similar to that of logistic regression.

Aged↗
PubMed1997

Using neural networks to automatically detect brain tumours in MR images.

Computer vision has been applied to many medical imaging problems with the aim of providing clinical tools to aid medical professionals. We present work being carried out to develop one such system to automatically detect a specific type of brain tumour from head MR images. The tumour under consideration is an acoustic neuroma, which is a benign tumour occurring in the acoustic canals. The hybrid system developed integrates neural networks with more conventional techniques used for computer vision tasks. A database of MR images from 50 patients has been assembled and the acoustic neuromas present in the images have been labelled by hand. Using this data, neural networks (MLPs) have been developed to classify the images at the pixel level to achieve a targeted segmentation. The data used to train and test the MLPs developed, consists of the grey levels of a square of pixels, the pixel to be classified being the centre pixel, together with its global position in the image. The initial pixel level segmentation is refined by a series of conventional techniques. It is combined with an edge-region based segmentation and a morphological operation is applied to the result. This processing produces clusters of adjacent regions, which are considered to be candidate tumour regions. For each possible combination of these regions, features are measured and presented to neural networks which have been trained to identify structures corresponding to acoustic neuromas. Using this approach, all the acoustic neuromas are identified together with three false positive errors.

Automation↗
PubMed1998

Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network.

Epiluminescence microscopy (ELM) is a non-invasive technique for in vivo examination which can provide additional criteria for the clinical diagnosis of pigmented skin lesions (PSLs). In the present study we attempt to determine whether PSLs can be automatically diagnosed by an integrated computerized system. This system should recognize the PSL, automatically extract features and use these features in training an artificial neural network, which should--if sufficiently trained--be capable of recognizing and classifying a new PSL without human aid. One hundred and twenty images of randomly selected histologically proven PSLs (33 common naevi, 48 dysplastic naevi and 39 malignant melanomas) were used in this study. The images were digitally obtained and the morphological features of the PSLs were extracted electronically without human assistance. The numerical data were then divided into learning and testing cases and linked to an artificial neural network for training and for further classification of lesions that the system had not been trained on. Our results show that the computerized system was able to automatically identify 95% of the PSLs presented. The sensitivity and specificity of the computerized system were 90% and 74% respectively. In contrast, when differentiating between individual types of lesions, the system performed at true positive rates of only 38% for malignant melanoma, 62% for dysplastic naevi and 33% for common naevi. Our data indicate that (1) ELM images of PSLs provide an excellent source for digital image analysis; (2) the vast majority of PSLs can be correctly identified by a relatively simple (and thus not "intelligent") application of digital image analysis; (3) automatic feature extraction based mainly on ABCD rules provides reliable data on the distinction between benign and malignant PSLs; and (4) there is evidence that artificial neural networks can be trained to adequately discriminate between benign and malignant PSLs.

Dysplastic Nevus Syndrome↗
PubMed1989

Dynamical neural network organization of the visual pursuit system.

The central nervous system is a parallel dynamical system which connects sensory input with motor output for the performance of visual tracking. This paper applies elementary control system tools to extend dynamical neural network models to the visual smooth pursuit system. Observed eye position responses to target motions and characteristics of the plant (eye muscles and orbital mechanics) place dynamical constraints on the interposed neural network controller. In the process of constructing a model for the controller, we show two previous pursuit system models, using efference copy and feedforward compensation, are equivalent from an input-output standpoint. We introduce a controller model possessing a potentially highly parallel implementation and offer an example with supporting neural firing rate data. Changes in time delays or other system dynamics are expected to lead to compensatory adaptive changes in the controller. A scheme to noninvasively simulate such changes in system dynamics was developed. Actual physiologic data of adaptive responses to increased time delay is presented as an example of the utility of this parallel controller. Compensatory changes in our parallel controller model are easily predicted. These results suggest a productive interaction between neural network modeling, neurophysiology, and control systems engineering.

Animals↗
PubMed2001

Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes.

Electronic structures of small peptides were calculated 'ab initio' with the help of Density Functional Theory (DFT) and molecular dynamics that rendered a set of conformational states of the peptides. For the structures of these states it was possible to derive atomic polar tensors that allowed us to construct vibrational spectra for each of the conformational states with low energy. From the spectra, neural networks could be trained to distinguish between the various states and thus be able to generate a larger set of relevant structures and their relation to secondary structures of the peptides. The calculations were done both with solvent atoms (up to ten water molecules) and without, and hence the neural networks could be used to monitor the influence of the solvent on hydrogen bond formation. The calculations at this stage only involved very short peptide fragments of a few alanine amino acids but already at this stage they could be compared with reasonable agreements to experiments. The neural networks are shown to be good in distinguishing the different conformers of the small alanine peptides, especially when in the gas phase. Also the task of predicting protein fold-classes, defined from line-geometry, seems promising.

Algorithms↗
PubMed2003

Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance.

The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tablets formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root mean square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f1 2.465) and similarity (f2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.

Aspirin↗
PubMed1999

Robust QSAR models using Bayesian regularized neural networks.

We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.

Drug Design↗
PubMed2003

Self-organized critical neural networks.

A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters.

Journal Article↗
PubMedDate not supplied

Aqueous solubility prediction of drugs based on molecular topology and neural network modeling.

A method for predicting the aqueous solubility of drug compounds was developed based on topological indices and artificial neural network (ANN) modeling. The aqueous solubility values for 211 drugs and related compounds representing acidic, neutral, and basic drugs of different structural classes were collected from the literature. The data set was divided into a training set (n = 160) and a randomly chosen test set (n = 51). Structural parameters used as inputs in a 23-5-1 artificial neural network included 14 atom-type electrotopological indices and nine other topological indices. For the test set, a predictive r2 = 0.86 and s = 0.53 (log units) were achieved.

Models, Chemical↗
PubMed1993

Application of neural networks in the QSAR analysis of percent effect biological data: comparison with adaptive least squares and nonlinear regression analysis.

Artificial neural networks (ANN) can be used for the direct QSAR analysis of percent effect biological data, thus avoiding the bias introduced by arbitrarily chosen classes and the loss of information due to prior classification. For two data sets the ANN results are compared with those obtained by adaptive least squares and nonlinear regression analyses. In comparison with the other methods the neural network shows higher predictive power and does not require an explicit equation relating the observed effect to physicochemical descriptors.

Animals↗
PubMed1994

Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study.

There is controversy about how prostate cancer screening tests should best be used because of the false-negative and false-positive results. There also is controversy about prostate cancer treatment because of errors in tumor staging, uncertainty about treatment efficacy and the variable natural history of the disease. We sought to determine in a pilot study whether artificial neural networks would be helpful to predict biopsy results in men with abnormal screening test(s) and to predict treatment outcome after radical prostatectomy. To predict biopsy results, we extracted data from a prostate specific antigen (PSA) based screening study data base in 1,787 men with a serum PSA concentration of more than 4.0 ng./ml. (approximately 40% of the men also had suspicious findings on digital rectal examination). To predict cancer recurrence after radical prostatectomy, we extracted data from a random sample of 240 patients selected from a data base of men who had undergone radical prostatectomy. The neural network predicted the biopsy result with 87% overall accuracy, and its output threshold could be adjusted to achieve the desired tradeoff between sensitivity and specificity. It also predicted tumor recurrence with 90% overall accuracy. We conclude that trained neural networks may be useful in decision making for prostate cancer patients.

Aged↗
PubMed2002

On unique representations of certain dynamical systems produced by continuous-time recurrent neural networks.

This article extends previous mathematical studies on elucidating the redundancy for describing functions by feedforward neural networks (FNNs) to the elucidation of redundancy for describing dynamical systems (DSs) by continuous-time recurrent neural networks (RNNs). In order to approximate a DS on R(n) using an RNN with n visible units, an n-dimensional affine neural dynamical system (A-NDS) can be used as the DS actually produced by the above RNN under an affine map from its visible state-space R(n) to its hidden state-space. Therefore, we consider the problem of clarifying the redundancy for describing A-NDSs by RNNs and affine maps. We clarify to what extent a pair of an RNN and an affine map is uniquely determined by its corresponding A-NDS and also give a nonredundant sufficient search set for the DS approximation problem based on A-NDS.

Journal Article↗
PubMed1998

Artificial neural network classification based on capillary electrophoresis of urinary nucleosides for the clinical diagnosis of tumors.

Nucleosides in human urine have been studied frequently as a possible biomedical marker for cancers, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. A capillary electrophoretic method that can quantitatively analyze urinary normal and modified nucleosides in less than 40 min with a good resolution and sufficient sensitivity has been developed. Twelve kinds of normal and modified nucleosides were determined in urine samples from 25 healthy persons and 25 cancer patients of 14 kinds of cancers. Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached to 100% and above 85% of the members in the predicting set were correctly classified. In addition, the neural network technique was compared with methods of the principal component analysis and the canonical discriminant analysis. The results demonstrate that the predictive ability of the artificial neural network is stronger than the others in this study.

Biomarkers, Tumor↗
PubMed1994

Automatic detection method of P300 waveform in the single sweep records by using a neural network.

Event-related potentials (ERPs) are responses related to the recognition of certain stimuli. P300 is the most important positive component in the ERP and appears around 300 ms after the target stimulus in the oddball paradigm. In our previous work, we proposed a method for the automatic detection of the P300 waveform in single-sweep records by using a correlation technique. However, determination of the threshold values of the P300 waveform for the correlation study was not an easy task. In skirting this problem, we developed an automatic method of detecting a single-sweep P300 waveform by using an artificial neural network. We selected appropriate characteristic parameters of positive peaks as input signals for the input layer units, and the weights between the layers were determined by using the back-propagation algorithm. The neural network for P300 detection was obtained automatically, based on the data of ERPs obtained from 11 healthy males, and gave substantial accuracy for P300 detection. Furthermore, by using this neural network we clarified the way in which the P300 waveform is judged visually by each inspector.

Acoustic Stimulation↗