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PubMed1992

Sequences of smoothly correlated patterns in neural networks with random transmission delays.

A neural network with a broad distribution of transmission delays was used to study numerically the retrieval of sequences having several types of correlations between successive patterns. In the case of sequences consisting of patterns correlated for finite time, the quality of retrieval was found to be (more or less) independent of the pattern correlation width and the delay distribution. On the other hand the quality of retrieval is dependent on these factors in the case of sequences with pattern correlation functions with long time 'tails'. Finally we have studied to what extent the storage capacity depends on the pattern correlation function and the delay distribution.

Animals↗
PubMed1991

Topological maps of protein sequences.

A new method based on neural networks to cluster proteins into families is described. The network is trained with the Kohonen unsupervised learning algorithm, using matrix pattern representations of the protein sequences as inputs. The components (x, y) of these 20 x 20 matrix patterns are the normalized frequencies of all pairs xy of amino acids in each sequence. We investigate the influence of different learning parameters in the final topological maps obtained with a learning set of ten proteins belonging to three established families. In all cases, except in those where the synaptic vectors remains nearly unchanged during learning, the ten proteins are correctly classified into the expected families. The classification by the trained network of mutated or incomplete sequences of the learned proteins is also analysed. The neural network gives a correct classification for a sequence mutated in 21.5% +/- 7% of its amino acids and for fragments representing 7.5% +/- 3% of the original sequence. Similar results were obtained with a learning set of 32 proteins belonging to 15 families. These results show that a neural network can be trained following the Kohonen algorithm to obtain topological maps of protein sequences, where related proteins are finally associated to the same winner neuron or to neighboring ones, and that the trained network can be applied to rapidly classify new sequences. This approach opens new possibilities to find rapid and efficient algorithms to organize and search for homologies in the whole protein database.

Amino Acid Sequence↗
PubMed1991

A neural network model of the cerebellar cortex performing dynamic associations.

The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the Golgi-granule cell system. A perceptron association is then performed on these sequences of patterns by the Purkinje cell layer. The model conforms with important biological constraints, such as the known excitatory or inhibitory nature of the various synapses. Also, as suggested by experimental evidence, the synaptic plasticity underlying the learning ability of the model, is confined to the parallel fiber--Purkinje cell synapses, and takes place under the control of the climbing fibers. The result is a neural network model, constructed according to the anatomy of the cerebellar cortex, and capable of learning and retrieval of temporal sequences of patterns. It provides a framework to represent and interpret properties of learning and control of movements by the cerebellum, and to assess the capacity of formal neural network techniques for modeling of real neural systems.

Animals↗
PubMed1991

Langevin machine: a neural network based on stochastically justifiable sigmoidal function.

In neural networks the activation process controls the output as a nonlinear function of the input; and, this output remains bounded between limits as decided by a logistic function known as the sigmoid (S-shaped). Presently, by applying the considerations of Maxwell-Boltzmann statistics, the Langevin function is shown as the appropriate and justifiable sigmoid (instead of the conventional hyperbolic tangent function) to depict the bipolar nonlinear logic-operation enunciated by the collective stochastical response of artificial neurons under activation. That is, the graded response of a large network of 'neurons' such as Hopfield's can be stochastically justified via the proposed model. The model is consistent with the established link between the Hopfield model and the statistical mechanics. The Langevin function (in lieu of conventional hyperbolic tangent and/or exponential sigmoids) in determining nonlinear decision boundaries, in characterizing the neural networks by the Langevin machine versus the Boltzmann machine, in sharpening and annealing schedules and in the optimization of nonlinear detector performance are discussed.

Animals↗
PubMed1991

Inhibition in the nervous system: models of its roles in choice and context determination.

Neural inhibition has often been regarded as playing an important role in stabilizing and tuning the responses of networks of excitatory neurons. Some partial quantitative bases for this qualitative notion are discussed in the context of current neural network models. Such neural network principles as associative learning, competition, opponent processing, and interlevel resonant feedback are explained and related to behavioral and neurochemical data. Tentative analogies of parts of these model networks with specific neurotransmitter systems are explored; these analogies are likely to become more precise as the networks are further refined.

Animals↗
PubMed1992

Parallel distributed processing and neuropsychology: a neural network model of Wisconsin Card Sorting and verbal fluency.

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.

Attention↗
PubMed1992

A practical application of neural network analysis for predicting outcome of individual breast cancer patients.

It has been previously shown that Neural Networks can be trained to recognize individual breast cancer patients at high and low risk for recurrent disease and death. This paper expands on the initial investigation and shows that by coding time as one of the prognostic variables, a Neural Network can use censored survival data to predict patient outcome over time. In this demonstration a Neural Network was trained, tested, and validated using censored survival data from a group of 1373 patients with node-positive breast cancer. The Neural Network method predicted patient outcome as accurately as Cox Regression modeling. The final Neural Network model can be presented with a patient's prognostic information and make a series of predictions about probability of relapse at different times of follow-up, allowing one to draw survival probability curves for individual patients.

Adult↗
PubMed1992

Quantitative measurement of two-component pH-sensitive colorimetric spectra using multilayer neural networks.

The purpose of this research was to develop a noise tolerant and faster processing approach for in vivo and in vitro spectrophotometric applications where distorted spectra are difficult to interpret quantitatively. A PC based multilayer neural network with a sigmoid activation function and a generalized delta learning rule was trained with a two component (protonated and unprotonated form) pH-dependent spectrum generated from microspectrophotometry of the vital dye neutral red (NR). The network makes use of the digitized absorption spectrum between 375 and 675 nm. The number of nodes in the input layer was determined by the required resolution. The number of output nodes determined the step size of the quantization value used to distinguish the input spectra (i.e. defined the number of distinct output steps). Mathematic analysis provided the conditions for which this network is guaranteed to converge. Simulation results showed that features of the input spectrum were successfully identified and stored in the weight matrix of the input and hidden layers. After convergent training with typical spectra, a calibration curve was constructed to interpret the output layer activity and therefore, predict interpolated pH values of unknown spectra. With its built-in redundant presentation, this approach needed no preprocessing procedures (baseline correction or intensive signal averaging) normally used in multicomponent analyses. The identification of unknown spectra with the activities of the output layer is a one step process using the convergent weight matrix. After learning from examples, real time applications can be accomplished without solving multiple linear equations as in the multiple linear regression method.(ABSTRACT TRUNCATED AT 250 WORDS)

Hydrogen-Ion Concentration↗
PubMed1992

Noninvasive diagnosis of coronary artery disease using a neural network algorithm.

This study examines the utility of neural networks for detecting coronary artery disease noninvasively by using the clinical examination variables and extracting useful information from the diastolic heart sounds associated with coronary occlusions. It has been widely reported that coronary stenoses produce sounds due to the turbulent blood flow in these vessels. These complex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on the poles and power spectral density function (PSD) of the Autoregressive (AR) method after Adaptive Line Enhancement (ALE) method. In addition, some physical examination variables such as sex, age, body weight, smoking condition, diastolic pressure, systolic pressure and derivation from them were included in the feature vector. This feature vector was used as the input pattern to the neural network. The analysis was studied on one hundred recordings (63 abnormal, 37 normals). The network correctly identified 84% of the subjects with coronary artery disease and 89% of the normal subjects.

Algorithms↗
PubMed1992

Review of neural network applications in medical imaging and signal processing.

The current applications of neural networks to in vivo medical imaging and signal processing are reviewed. As is evident from the literature neural networks have already been used for a wide variety of tasks within medicine. As this trend is expected to continue this review contains a description of recent studies to provide an appreciation of the problems associated with implementing neural networks for medical imaging and signal processing.

Diagnostic Imaging↗
PubMed1992

New method of automated sleep quantification.

Since its discovery some 50 years ago, the electro-encephalogram (EEG) has formed the basis for classification of sleep into several stages, either laboriously performed by visual examination of the EEG and related signals or, more recently, by automated techniques. Both visual scoring and most automated analyses are highly subjective and rely on application of a predefined set of rules. A method of analysing the EEG which requires no such application of rules and aims to give some indication of the dynamics of sleep in humans is proposed in the paper.

Adult↗
PubMed1991

Application of an artificial neural network in radiographic diagnosis.

The description of 44 cases of bone tumors was used by an artificial neural network to rank the likelihood of 55 possible pathologic diagnoses. The performance of the artificial neural network was compared with the performance of experienced (3 or more years of radiology training) residents and inexperienced (less than 1 year of radiology training) residents. The artificial neural network was trained using descriptions of 110 radiographs of bone tumors with known diagnoses. The descriptions of a separate set of 44 cases were used to test the neural network. The neural network ranked 55 possible pathologic diagnoses on a scale from 1 to 55. Experienced and inexperienced residents also ranked the possible diagnoses in the same 44 cases. Inexperienced residents had a significantly lower mean proportion of diagnoses ranked first or second than did the neural network. Experienced residents had a significantly higher proportion of correct diagnoses ranked first than did the network. Otherwise, a significant difference between the performance of the network and experienced or inexperienced residents was not identified. These results demonstrate that artificial neural networks can be trained to classify bone tumors. Whether neural network performance in classification of bone tumors can be made accurate enough to assist radiologists in clinical practice remains an open question. These preliminary results indicate that further investigation of this technology for interpretation assistance is warranted.

Bone Neoplasms↗
PubMed1992

Artificial neural networks in bioprocess state estimation.

The application of artificial neural networks to the estimation and prediction of bioprocess variables is presented in this paper. A neural network methodology is discussed, which uses environmental and physiological information available from on-line sensors, to estimate concentration of species in the bioreactor. Two case studies are presented, both based on the ethanol production by Zymomonas mobilis. An efficient optimization algorithm which reduces the number of iterations required for convergence is proposed. Results are presented for different training sets and different training methodologies. It is shown that the neural network estimator provides good on-line bioprocess state estimations.

Biomedical Engineering↗
PubMedDate not supplied

A PC-based neural network for on-line measurement of regional cerebral blood flow.

The measurement of regional cerebral blood flow has been used in diagnoses for many cerebral disorders. However, the complexity of data processing makes the technique only possible for off-line study. This paper explores a PC-based neural net technique for on-line determination of the blood flow rate. Experimental results justify the efficiency of the proposed methodology and raise the possibility of designing a portable system for time-critical applications.

Cerebrovascular Circulation↗
PubMed1992

Automated interictal EEG spike detection using artificial neural networks.

Feed-forward, error-back-propagation artificial neural networks were applied to recognition of epileptiform patterns in the EEG. The inherent network properties of generalization and variability tolerance were effective in identifying wave forms that differed from the training patterns but still maintained 'epileptiform' spatio-temporal characteristics. The certainty of recognition was measured as a continuous function with a range of 0-1. Two levels of certainty (0.825 and 0.900) were used to indicate recognition of spikes and sharp waves (SSW). An average 94.2% (+/- 7.3) of the SSW were recognized; 20.9% (+/- 22.9) of all recognized SSW were false-positive recognitions. The time required for pattern recognition was well within the time required for digitizing the analogue data. This study provides evidence that neural network technology is, in principle, an effective pattern recognition strategy for identification of epileptiform transients in the EEG. The analysis is sufficiently rapid to be of potential value as a strategy for data reduction of long recordings stored on bulk media.

Electroencephalography↗
PubMed1992

Prediction of the side of hand movements from single-trial multi-channel EEG data using neural networks.

Thirty channels of EEG data were recorded prior to voluntary right or left hand movements. Event-related desynchronization (ERD) was quantified in the 8-10 Hz and 10-12 Hz bands in single-trial data and used as training input for a neural network comprised of a learning vector quantizer (LVQ). After a training period, the network was able to predict the side of hand movement from single-trial EEG data recorded prior to movement onset.

Brain↗
PubMed1992

The use of artificial neural networks to estimate speech intelligibility from acoustic variables: a preliminary analysis.

Previous research has used regression analysis to attempt to predict the intelligibility of hearing-impaired speakers from acoustic speech parameters. Improvement of prediction may be achieved by the use of computerized artificial neural networks to process mathematically the acoustic input variables as part of the intelligibility process. A preliminary scheme for estimating speech intelligibility from acoustic parameters using a neural network is outlined and preliminary data illustrate its use.

Adult↗