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PubMed1992

Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks.

The main difficulties in reliable automated detection of the K-complex wave in EEG are its close similarity to other waves and the lack of specific characterization criteria. We present a feature-based detection approach using neural networks that provides good agreement with visual K-complex recognition: a sensitivity of 90% is obtained with about 8% false positives. The respective contribution of the features and that of the neural network is demonstrated by comparing the results to those obtained with i) raw EEG data presented to neural networks, and ii) features presented to Fisher's linear discriminant.

Electroencephalography↗
PubMed1994

Neural network approaches to visual motion perception.

This paper concerns certain difficult problems in image processing and perception: neurocomputation of visual motion information. The first part of this paper deals with the spatial physiological integration by the figure-ground discrimination neural network in the visual system of the fly. We have outlined the fundamental organization and algorithms of this neural network, and mainly concentrated on the results of computer simulations of spatial physiological integration. It has been shown that the gain control mechanism, the nonlinearity of synaptic transmission characteristic, the interaction between the two eyes, and the directional selectivity of the pool cells play decisive roles in the spatial physiological integration. In the second part, we have presented a self-organizing neural network for the perception of visual motion by using a retinotopic array of Reichardt's motion detectors and Kohonen's self-organizing maps. It has been demonstrated by computer simulations that the network is able to learn to solve the ambiguities given by local motion detection mechanism. The resultant self-organized configuration in the output layer is resembling direction selective columns which first appear in area MT of the primate visual system. It has been explored that the spatio-temporal coherences, mapping, cooperation, competition, and Hebb rule are the basic neural principles for visual motion perception.

Animals↗
PubMed1992

Visuomotor transformations underlying arm movements toward visual targets: a neural network model of cerebral cortical operations.

We propose a biologically realistic neural network that computes coordinate transformations for the command of arm reaching movements in 3-D space. This model is consistent with anatomical and physiological data on the cortical areas involved in the command of these movements. Studies of the neuronal activity in the motor (Georgopoulos et al., 1986; Schwartz et al., 1988; Caminiti et al., 1990a) and premotor (Caminiti et al., 1990b, 1991) cortices of behaving monkeys have shown that the activity of individual arm-related neurons is broadly tuned around a preferred direction of movements in 3-D space. Recent data demonstrate that in both frontal areas (Caminiti et al., 1990a,b, 1991) these cell preferred directions rotate with the initial position of the arm. Furthermore, the rotation of the population of preferred directions precisely corresponds to the rotation of the arm in space. The neural network model computes the motor command by combining the visual information about movement trajectory with the kinesthetic information concerning the orientation of the arm in space. The appropriate combination, learned by the network from spontaneous movement, can be approximated by a bilinear operation that can be interpreted as a projection of the visual information on a reference frame that rotates with the arm. This bilinear combination implies that neural circuits converging on a single neuron in the motor and premotor cortices can learn and generalize the appropriate command in a 2-D subspace but not in the whole 3-D space. However, the uniform distribution of cell preferred directions in these frontal areas can explain the computation of the correct solution by a population of cortical neurons. The model is consistent with the existing neurophysiological data and predicts how visual and somatic information can be combined in the different processing steps of the visuomotor transformation subserving visual reaching.

Animals↗
PubMed1997

Training Dynamics and Neural Network Performance.

We use an analysis of a simple model of recurrent network dynamics to gain qualitative insights into the training dynamics of feedforward multilayer perceptrons (MLPs) used for classification. These insights suggest changes to the training methods used for MLPs that improve network performance significantly. In previous work, the probabilistic neural network (PNN) was shown to provide better zero-reject error performance on character and fingerprint classification problems than radial basis function and MLP-based neural network methods. We will show that performance equal to or better than PNN can be achieved with a single three-layer MLP by making fundamental changes in the network optimization strategy. These changes are: 1) use of neuron activation functions, which reduce the probability of singular Jacobians; 2) use of successive regularization to constrain the volume of the minimized weight space; 3) use of Boltzmann pruning to constrain the dimension of the weight space; 4) use of Prior class probabilities to normalize all error calculations, so that statistically significant samples of rare but important classes can be included without distorting the error surface. All four of these changes are made in the inner loop of a conjugate gradient optimization iteration and are intended to simplify the training dynamics of the optimization. On handprinted digits and fingerprint classification problems these modifications improve error-reject performance by factors between 2 and 4, and reduce network size by 40-60%. Copyright 1997 Elsevier Science Ltd.

Journal Article↗
PubMed1989

Associative memory neural network with low temporal spiking rates.

We describe a modified attractor neural network in which neuronal dynamics takes place on a time scale of the absolute refractory period but the mean temporal firing rate of any neuron in the network is lower by an arbitrary factor that characterizes the strength of the effective inhibition. It operates by encoding information on the excitatory neurons only and assuming the inhibitory neurons to be faster and to inhibit the excitatory ones by an effective postsynaptic potential that is expressed in terms of the activity of the excitatory neurons themselves. Retrieval is identified as a nonergodic behavior of the network whose consecutive states have a significantly enhanced activity rate for the neurons that should be active in a stored pattern and a reduced activity rate for the neurons that are inactive in the memorized pattern. In contrast to the Hopfield model the network operates away from fixed points and under the strong influence of noise. As a consequence, of the neurons that should be active in a pattern, only a small fraction is active in any given time cycle and those are randomly distributed, leading to reduced temporal rates. We argue that this model brings neural network models much closer to biological reality. We present the results of detailed analysis of the model as well as simulations.

Animals↗
PubMed1990

Simulation study on dynamics transition in neuronal activity during sleep cycle by using asynchronous and symmetry neural network model.

We have found that single neuronal activities in different regions in the brain commonly exhibit the distinct dynamics transition during sleep-waking cycle in cats. Especially, power spectral densities of single neuronal activities change their profiles from the white to the 1/f along with sleep cycle from slow wave sleep (SWS) to paradoxical sleep (PS). Each region has different neural network structure and physiological function. This suggests a globally working mechanism may be underlying the dynamics transition we concern. Pharmacological studies have shown that a change in a wide-spread serotonergic input to these regions possibly causes the neuronal dynamics transition during sleep cycle. In this paper, based on these experimental results, an asynchronous and symmetry neural network model including inhibitory input, which represents the role of the serotonergic system, is utilized to examine the reality of our idea that the inhibitory input level varying during sleep cycle induce that transition. Simulation results show that the globally applied inhibitory input can control the dynamics of single neuronal state evolution in the artificial neural network: 1/f-like power spectral density profiles result under weak inhibition, which possibly corresponds to PS, and white profiles under strong inhibition, which possibly corresponds to SWS. An asynchronous neural network is known to change its state according to its energy function. The geometrical structure of network energy function is thought to vary along with the change in inhibitory level, which is expected to cause the dynamics transition of neuronal state evolution in the network model. These simulation results support the possibility that the serotonergic system is essential for the dynamics transition of single neuronal activities during sleep cycle.

Animals↗
PubMed2001

Simulation of biomass gasification with a hybrid neural network model.

Gasification of several types of biomass has been conducted in a fluidized bed gasifier at atmospheric pressure with steam as the fluidizing medium. In order to obtain the gasification profiles for each type of biomass, an artificial neural network model has been developed to simulate this gasification processes. Model-predicted gas production rates in this biomass gasification processes were consistent with the experimental data. Therefore, the gasification profiles generated by neural networks are considered to have properly reflected the real gasification process of a biomass. Gasification profiles identified by neural network suggest that gasification behavior of arboreal types of biomass is significantly different from that of herbaceous ones.

Biomass↗
PubMed2005

A novel neural network for variational inequalities with linear and nonlinear constraints.

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the sufficient and necessary conditions of the solution, this paper presents a novel neural network model for solving variational inequalities with linear and nonlinear constraints. Three sufficient conditions are provided to ensure that the proposed network with an asymmetric mapping is stable in the sense of Lyapunov and converges to an exact solution of the original problem. Meanwhile, the proposed network with a gradient mapping is also proved to be stable in the sense of Lyapunov and to have a finite-time convergence under some mild condition by using a new energy function. Compared with the existing neural networks, the new model can be applied to solve some nonmonotone problems, has no adjustable parameter, and has lower complexity. Thus, the structure of the proposed network is very simple. Since the proposed network can be used to solve a broad class of optimization problems, it has great application potential. The validity and transient behavior of the proposed neural network are demonstrated by several numerical examples.

Algorithms↗
PubMed1995

Human-like dynamic programming neural networks for dynamic time warping speech recognition.

This paper presents a human-like dynamic programming neural network method for speech recognition using dynamic time warping. The networks are configured, much like human's, such that the minimum states of the network's energy function represent the near-best correlation between test and reference patterns. The dynamics and properties of the neural networks are analytically explained. Simulations for classifying speaker-dependent isolated words, consisting of 0 to 9 and A to Z, show that the method is better than conventional methods. The hardware implementation of this method is also presented.

Humans↗
PubMed1991

Neural network optimization for E. coli promoter prediction.

Methods for optimizing the prediction of Escherichia coli RNA polymerase promoter sequences by neural networks are presented. A neural network was trained on a set of 80 known promoter sequences combined with different numbers of random sequences. The conserved -10 region and -35 region of the promoter sequences and a combination of these regions were used in three independent training sets. The prediction accuracy of the resulting weight matrix was tested against a separate set of 30 known promoter sequences and 1500 random sequences. The effects of the network's topology, the extent of training, the number of random sequences in the training set and the effects of different data representations were examined and optimized. Accuracies of 100% on the promoter test set and 98.4% on the random test set were achieved with the optimal parameters.

Artificial Intelligence↗
PubMed1987

A hierarchical neural-network model for control and learning of voluntary movement.

In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed2003

[Application of artificial neural network to simultaneous spectrofluorimetric determination of vitamin B1, B2 and B6].

By means of artificial neural network and Levenberg-Marquardt back-propagation training algorithm, the three components of vitamin B1, B2, B6 were determined simultaneously, in which the spectra overlapped. In the range of 390-520 nm, the fluorescence intensities at 15 wavelengths were taken as a character of the artificial neural network, and samples were arranged by the method of equality design. The mean recoveries of vitamin B1, vitamin B2, and vitamin B6 were 99.86%, 99.60% and 99.49% respectively. The RSDs of the results were 1.7%, 1.6%, and 1.7%.

Algorithms↗
PubMed2005

Human acetylcholinesterase inhibitors: electronic-topological and neural network approaches to the structure-activity relationships study.

The Electronic-Topological (ETM) and Neural Network methods were applied to the study of the "structure-acetylcholinesterase (AChE) inhibitor activity" relationships for a series of physostigmine and N-benzylpiperidine derivatives. Molecular fragments specific for active compounds and breaks of activity were calculated for human AChE by applying the ETM and Neural Network methods. Requirements necessary for a compound to be active were formulated; they are the result of detailed analysis of all compounds under study. A comparative study of the activity features found for human AChE was performed.

Acetylcholinesterase↗
PubMed2006

A neural network controller for the path tracking control of a hopping robot involving time delays.

In this paper a hopping robot motion with offset mass is discussed. A mathematical model has been considered and an efficient single layered neural network has been developed to suit to the dynamics of the hopping robot, which ensures guaranteed tracking performance leading to the stability of the otherwise unstable system. The neural network takes advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot parameters. Time delays in the control mechanism play a vital role in the motion of hopping robots. The present work also enables us to estimate the maximum time delay admissible with out losing the guaranteed tracking performance. Further this neural network does not require offline training procedures. The salient features are highlighted by appropriate simulations.

Algorithms↗
PubMed2005

A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7.

An artificial neural network (ANN) model was developed to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7 in a mayonnaise-type system. Temperature, pH, acetic acid, sucrose and salt were the inputs to a three-layer back-propagation neural network. The ANN model was trained using the data-set of McKellar et al. [2002. A probability model describing the interface between survival and death of E. coli O157:H7 in a mayonnaise model system. Food Microbiol. 19, 235-247] that consisted of 1,820 treatment combinations from controlled experiments with a cocktail of five strains of E. coli O157:H7. After training, the model correctly predicted the growth/no-growth in 1,810 combinations (99.5%) with 8 false positives and 2 false negatives, and survival/death in 1,804 combinations (99.1%) with 13 false positives and 3 false negatives. Classification accuracy was validated using additional literature data-sets for growth of E. coli O157:H7 under various environmental conditions. The ANN model accurately predicted the survival/death in 27 of 30 cases (90%) in experimental mayonnaise inoculated with E. coli O157:H7, with 3 fail-positive predictions and all observed growth (100%). Simulations were used to estimate the influence of incubation temperature on survival and growth for specific combinations of acetic acid, salt, pH and sucrose. The ANN model is recommended as an alternative tool for classification of survival and growth conditions in predictive microbiology.

Colony Count, Microbial↗
PubMed2004

Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow.

The paper is illustrating how the general data mining methodology may be adapted to provide solutions to the problem of high throughput virtual screening of organic chemicals for possible acute toxicity to the fathead minnow fish. The present approach involves mining fragment information from chemical structures and is using probabilistic neural networks to model the relationship between structure and toxicity. Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models. The mathematical principles supporting their use for value prediction purposes are clarified and their peculiarities discussed. As part of the research phase of the data mining process, a dataset consisting of 800 structures and associated fathead minnow (Pimephales promelas) 96-h LC50 acute toxicity endpoint information is used for both the purpose of identifying an advantageous combination of fragment descriptors and for training the neural networks. As a result, two powerful models are generated. Model 1 implements the basic PNN with Gaussian kernel (statistical corrections included) while Model 2 implements the PNN with Gaussian kernel and separated variables. External validation is performed using a separate dataset consisting of 86 structures and associated toxicity information. Both learning and generalization capabilities of the two models are investigated and their limitations discussed.

Animals↗
PubMed1996

Neural networks in high-performance liquid chromatography optimization: response surface modeling.

The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method (LOOM). Data sets of linear and non-linear response surfaces (capacity factors) were taken from literature. The results show that neural networks offer promising possibilities in HPLC method development. The predictive results were better or comparable to those obtained with linear and non-linear regression models.

Chromatography, High Pressure Liquid↗
PubMedDate not supplied

Astronomical image segmentation by self-organizing neural networks and wavelets.

Standard image segmentation methods may not be able to segment astronomical images because their special nature. We present an algorithm for astronomical image segmentation based on self-organizing neural networks and wavelets. We begin by performing wavelet decomposition of the image. The segmentation process has two steps. In the first we separate the stars and other prominent objects using the second plane (w(2)) of the wavelet decomposition, which has little noise but retains enough signal to represent those objects. This method was as least as effective as the traditional source extraction methods in isolating bright objects both from the background and from extended sources. In the second step the rest of the image (extended sources and background) is segmented using a self-organizing neural network. The result is a predetermined number of clusters, which we associate with extended regions plus a small region for each star or bright object. We have applied the algorithm to segment images of both galaxies and planets. The results show that the simultaneous use of all the scales in the self-organizing neural network helps the segmentation process, since it takes into account not only the intensity level, but also both the high and low frequencies present in the image. The connectivity of the regions obtained also shows that the algorithm is robust in the presence of noise. The method can also be applied to restored images.

Algorithms↗