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PubMed2004

Global and local synchrony of coupled neurons in small-world networks.

Synchronous firing of neurons is thought to play important functional roles such as feature binding and switching of cognitive states. Although synchronization has mainly been investigated so far using model neurons with simple connection topology, real neural networks have more complex structures. Here we examine the behavior of pulse-coupled leaky integrate-and-fire neurons with various network structures. We first show that the dispersion of the number of connections for neurons influences dynamical behavior even if other major topological statistics are kept fixed. The rewiring probability parameter representing the randomness of networks bridges two spatially opposite frameworks: precise local synchrony and rough global synchrony. Finally, cooperation of the global connections and the local clustering property, which is prominent in small-world networks, forces synchrony of distant neuronal groups receiving coherent inputs.

Action Potentials↗

PubMed1997

Application of the fuzzy ART/MAP and MINMAX/MAP neural network models to radiographic image classification.

This paper concerns the classification analysis of exercise-induced lower leg pain by applying competitive neural network clustering and mapping techniques to type 1 and type 2 fuzzy descriptions of bone scan images of the tibia. The clusters are described and compared with each other and with the experts known classes that would be expected from medical findings. The discovered clusters provide training sets for supervised learning by an ARTMAP and similar neural network. These were used to classify the previously unclassified images and hence improve the classification process. The overall conclusion is that the use of the neural clustering methods has improved the classification process of the shin images despite the paucity of data and its inherent uncertainty.

Athletic Injuries↗

PubMed1993

Artificial neural networks for single photon emission computed tomography. A study of cold lesion detection and localization.

RATIONALE AND OBJECTIVES: An artificial neural network was developed for cold lesion detection and localization in single photon emission computed tomography (SPECT) images. METHODS: The network was trained for several noise levels and lesion sizes to identify lesions located in the center of small image neighborhoods. When scrolled across an image the trained network was able to identify cold abnormalities. The diagnostic performance of the technique was evaluated at two noise levels (50,000 and 100,000 counts/slice) and for two lesion sizes (radius: 1.0 cm and 1.5 cm) using the free-response operating characteristic (FROC) analysis. Furthermore, the same network was tested on a situation it was not trained on (80,000 counts/slice and a different reconstruction filter). RESULTS: The neural network showed high sensitivity and small false-positive rates per image for all test situations. These results suggest that neural networks are promising tools for computer-aided clinical diagnosis in SPECT:

Equipment Design↗

PubMed2004

Scalable closed-boundary analog neural networks.

In many pattern-classification and recognition problems, separation of different swarms of class representatives is necessary. As well, in function-approximation problems, neurons with a local area of influence have demonstrated measurable success. In our previous work, we have shown how intrinsic quadratic characteristics of traditional metal-oxide-semiconductor (MOS) devices can be used to implement hyperspherical discriminating surfaces in hardware-implemented neurons. In this work, we further extend the concept from quadratic forms to more-arbitrary closed-boundary shapes. Accordingly, we demonstrate how intrinsic characteristics of submicron MOS devices can be utilized to implement efficient pattern discriminators for various applications and, through representative simulations, show their success in some typical function-approximation problems. Further, we offer two mathematical interpretations of possible roles for these networks: Geometrically, we show that our networks employ closed hypercone shapes as their discriminating surfaces; analytically, we show that a set of these synapses connected to a common integrating body calculates the distance between their inputs and weight vectors using a power norm. The feasibility of the idea is practically investigated by design, implementation, and test of a three-dimensional (3-D) closed-boundary pattern classifier, fabricated in 0.35-microm complimentary MOS, whose results are reflected in this work.

Computers, Analog↗

PubMed2002

Online artery diameter measurement in ultrasound images using artificial neural networks.

An automated online technique is described for measurement of artery diameter in flow-mediated dilation (FMD) ultrasound (US) images, using artificial neural networks to identify and track artery walls. This allows FMD results to be calculated without the inherent delay of current retrospective methods. Two networks were trained to identify artery anterior and posterior walls using over 3200 examples from carotid artery images. Both networks correctly classified approximately 97% of the randomly selected test samples. The technique was verified using a physical model with absolute measurement error of -1.16% +/- 1.04% (mean +/- SD) over the diameter range 2 to 8 mm. Advantages of the technique include: online analysis; wall tracking optimisation before the study proper; measurement of diameter changes over the cardiac cycle; low FMD measurement variance; minimal image degradation; and no unwieldy image store. Measurement of artery diameter changes over the cardiac cycle was explored using simulated image sequences generated with a virtual US scanner.

Carotid Arteries↗

PubMed2001

Alopex-B: a new, simpler, but yet faster version of the alopex training algorithm.

Experimenting with some changes and simplifications to the Alopex algorithm, we obtained a new faster version (Alopex-B), that also shows lower failure rates on training attempts. Like Alopex, our version is network-architecture independent, does not require error or transfer functions to be differentiable, has a high potential for parallelism, and is stochastic (which helps avoid local minima), but unlike Alopex it follows no annealing scheme, and uses less parameters which makes it simpler to implement and to use.

Algorithms↗

PubMed2001

Segmentation of medical ultrasonic image using hybrid neural network.

Objective. To solve one of the most difficult problems in multi-dimensional reconstruction of medical ultrasonic images: image segmentation. Method. A new segmental method based on hybrid neural network was presented in this paper. The hybrid neural network comprised two phases. The first phase was Kohonens self-organization neural network, which was used to segment and label the image coarsely. The feature vectors of those pixels within a specified distance from the cluster centers were employed to train the second phase--a three-layer perception network using back-propagation (BP) technique. Then the trained BP network was used to label every pixel of the image. In the end, a post-processing stage was used to remove the small isolated points and smooth out the contours of the segmented image. Result. The segmented image had smooth continuous edges, few noises or speckles, and the contour of ventricle was clear and accurate. Conclusion. Our method could segment the ultrasonic images accurately and effectively, and had a lot of advantages compared to traditional methods. The unsupervised segmentation problems could be solved using supervised methods.

Cardiovascular Physiological Phenomena↗

PubMed1997

[Neuronal networks and psychopathology].

Within the past 10 years, computer simulations of neural networks have yielded new insights into the nature of information-processing neural systems. Since 1987, more and more network models relevant to psychiatry have been published, and such models have meanwhile been proposed for almost every psychopathological phenomenon. This paper provides an introduction to the theory of neural networks and gives examples of discoveries made via computer simulations. Network models of psychopathological phenomena are discussed with respect to formal thought disorder and delusions. The relevance of neural network models for psychopathological research and theory formulation is highlighted and it is argued that neural network modeling in psychopathology does not merely introduce a few mathematical gimmicks, but rather represents the beginning of a new basis for psychopathology.

Brain↗

PubMed1996

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

Neural Networks, Computer↗

PubMed2005

A robust classifier combined with an auto-associative network for completing partly occluded images.

This paper describes an approach for constructing a classifier which is unaffected by occlusions in images. We propose a method for integrating an auto-associative network into a simple classifier. As the auto-associative network can recall the original image from a partly occluded input image, we can employ it to detect occluded regions and complete the input image by replacing those regions with recalled pixels. By iterating this reconstruction process, the integrated network is able to classify target objects with occlusions robustly. To confirm the effectiveness of this method, we performed experiments involving face image classification. It is shown that the classification performance is not decreased, even if about 30% of the face image is occluded.

Artificial Intelligence↗

PubMed1998

Analytical and simulation results for stochastic Fitzhugh-Nagumo neurons and neural networks.

An analytical approach is presented for determining the response of a neuron or of the activity in a network of connected neurons, represented by systems of nonlinear ordinary stochastic differential equations--the Fitzhugh-Nagumo system with Gaussian white noise current. For a single neuron, five equations hold for the first- and second-order central moments of the voltage and recovery variables. From this system we obtain, under certain assumptions, five differential equations for the means, variances, and covariance of the two components. One may use these quantities to estimate the probability that a neuron is emitting an action potential at any given time. The differential equations are solved by numerical methods. We also perform simulations on the stochastic Fitzugh-Nagumo system and compare the results with those obtained from the differential equations for both sustained and intermittent deterministic current inputs with superimposed noise. For intermittent currents, which mimic synaptic input, the agreement between the analytical and simulation results for the moments is excellent. For sustained input, the analytical approximations perform well for small noise as there is excellent agreement for the moments. In addition, the probability that a neuron is spiking as obtained from the empirical distribution of the potential in the simulations gives a result almost identical to that obtained using the analytical approach. However, when there is sustained large-amplitude noise, the analytical method is only accurate for short time intervals. Using the simulation method, we study the distribution of the interspike interval directly from simulated sample paths. We confirm that noise extends the range of input currents over which (nonperiodic) spike trains may exist and investigate the dependence of such firing on the magnitude of the mean input current and the noise amplitude. For networks we find the differential equations for the means, variances, and covariances of the voltage and recovery variables and show how solving them leads to an expression for the probability that a given neuron, or given set of neurons, is firing at time t. Using such expressions one may implement dynamical rules for changing synaptic strengths directly without sampling. The present analytical method applies equally well to temporally nonhomogeneous input currents and is expected to be useful for computational studies of information processing in various nervous system centers.

Action Potentials↗

PubMed1995

High-quality compression of echographic images by neural networks and vector quantisation.

A compression method is presented for medical images based on neural networks and vector quantisation (VQ). The neural net is a perceptron and it is followed by some expressly designed VQ algorithms. The method devotes special attention to the quality of the reconstructed images and to the reduction of artefacts. The compression ratio obtained in the long run with echographic images is greater than 50:1 with good quality.

Algorithms↗

PubMed2002

Identification of Cryptosporidium parvum oocysts by an artificial neural network approach.

Microscopic detection of Cryptosporidium parvum oocysts is time-consuming, requires trained analysts, and is frequently subject to significant human errors. Artificial neural networks (ANN) were developed to help identify immunofluorescently labeled C. parvum oocysts. A total of 525 digitized images of immunofluorescently labeled oocysts, fluorescent microspheres, and other miscellaneous nonoocyst images were employed in the training of the ANN. The images were cropped to a 36- by 36-pixel image, and the cropped images were placed into two categories, oocyst and nonoocyst images. The images were converted to grayscale and processed into a histogram of gray color pixel intensity. Commercially available software was used to develop and train the ANN. The networks were optimized by varying the number of training images, number of hidden neurons, and a combination of these two parameters. The network performance was then evaluated using a set of 362 unique testing images which the network had never "seen" before. Under optimized conditions, the correct identification of authentic oocyst images ranged from 81 to 97%, and the correct identification of nonoocyst images ranged from 78 to 82%, depending on the type of fluorescent antibody that was employed. The results indicate that the ANN developed were able to generalize the training images and subsequently discern previously unseen oocyst images efficiently and reproducibly. Thus, ANN can be used to reduce human errors associated with the microscopic detection of Cryptosporidium oocysts.

Algorithms↗

PubMed2005

Use of artificial neural networks to accurately identify Cryptosporidium oocyst and Giardia cyst images.

Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images. Digitized images of C. parvum oocysts and G. lamblia cysts stained with various commercial IFA reagents were used as positive controls. The images were captured using a color digital camera at 400 x (total magnification), processed, and converted into a binary numerical array. A variety of "negative" images were also captured and processed. The ANN were developed using these images and a rigorous training and testing protocol. The Cryptosporidium oocyst ANN were trained with 1,586 images, while Giardia cyst ANN were trained with 2,431 images. After training, the best-performing ANN were selected based on an initial testing performance against 100 images (50 positive and 50 negative images). The networks were validated against previously "unseen" images of 500 Cryptosporidium oocysts (250 positive, 250 negative) and 282 Giardia cysts (232 positive, 50 negative). The selected ANNs correctly identified 91.8 and 99.6% of the Cryptosporidium oocyst and Giardia cyst images, respectively. These results indicate that ANN technology can be an alternate to having trained personnel for detecting these pathogens and can be a boon to underdeveloped regions of the world where there is a chronic shortage of adequately skilled individuals to detect these pathogens.

Animals↗

PubMed1996

A nonlinear Hebbian network that learns to detect disparity in random-dot stereograms.

An intrinsic limitation of linear, Hebbian networks is that they are capable of learning only from the linear pairwise correlations within an input stream. To explore what higher forms of structure could be learned with a nonlinear Hebbian network, we constructed a model network containing a simple form of nonlinearity and we applied it to the problem of learning to detect the disparities present in random-dot stereograms. The network consists of three layers, with nonlinear sigmoidal activation functions in the second-layer units. The nonlinearities allow the second layer to transform the pixel-based representation in the input layer into a new representation based on coupled pairs of left-right inputs. The third layer of the network then clusters patterns occurring on the second-layer outputs according to their disparity via a standard competitive learning rule. Analysis of the network dynamics shows that the second-layer units' nonlinearities interact with the Hebbian learning rule to expand the region over which pairs of left-right inputs are stable. The learning rule is neurobiologically inspired and plausible, and the model may shed light on how the nervous system learns to use coincidence detection in general.

Artificial Intelligence↗

PubMed2000

Neural networks in analog hardware--design and implementation issues.

This paper presents a brief review of some analog hardware implementations of neural networks. Several criteria for the classification of general neural networks implementations are discussed and a taxonomy induced by these criteria is presented. The paper also discusses some characteristics of analog implementations as well as some trade-offs and issues identified in the work reviewed. Parameters such as precision, chip area, power consumption, speed and noise susceptibility are discussed in the context of neural implementations. A unified review of various "VLSI friendly" algorithms is also presented. The paper concludes with some conclusions drawn from the analysis of the implementations presented.

Artificial Intelligence↗

PubMed1993

Neural network feature detector for real-time video signal processing.

The application of artificial neural networks to real-time image processing tasks requires the use of dedicated, high performance hardware. A linear array processor called HANNIBAL has been developed which implements the backpropagation neural learning algorithm on-chip. This paper considers the design of a complete neural system which integrates HANNIBAL into an existing image processing environment. The goals for the design of the system have been set partly by the primary application, namely feature recognition, but mainly by the desire for a flexible, high performance hardware tool for the study and evaluation of range of neural image processing applications.

Algorithms↗

PubMed2003

Topology representing network enables highly accurate classification of protein images taken by cryo electron-microscope without masking.

In single-particle analysis, a three-dimensional (3-D) structure of a protein is constructed using electron microscopy (EM). As these images are very noisy in general, the primary process of this 3-D reconstruction is the classification of images according to their Euler angles, the images in each classified group then being averaged to reduce the noise level. In our newly developed strategy of classification, we introduce a topology representing network (TRN) method. It is a modified method of a growing neural gas network (GNG). In this system, a network structure is automatically determined in response to the images input through a growing process. After learning without a masking procedure, the GNG creates clear averages of the inputs as unit coordinates in multi-dimensional space, which are then utilized for classification. In the process, connections are automatically created between highly related units and their positions are shifted where the inputs are distributed in multi-dimensional space. Consequently, several separated groups of connected units are formed. Although the interrelationship of units in this space are not easily understood, we succeeded in solving this problem by converting the unit positions into two-dimensional (2-D) space, and by further optimizing the unit positions with the simulated annealing (SA) method. In the optimized 2-D map, visualization of the connections of units provided rich information about clustering. As demonstrated here, this method is clearly superior to both the multi-variate statistical analysis (MSA) and the self-organizing map (SOM) as a classification method and provides a first reliable classification method which can be used without masking for very noisy images.

Algorithms↗