Connectional parameters determine multisensory processing in a spiking network model of multisensory convergence.

For the brain to synthesize information from different sensory modalities, connections from different sensory systems must converge onto individual neurons. However, despite being the definitive, first step in the multisensory process, little is known about multisensory convergence at the neuronal level. This lack of knowledge may be due to the difficulty for biological experiments to manipulate and test the connectional parameters that define convergence. Therefore, the present study used a computational network of spiking neurons to measure the influence of convergence from two separate projection areas on the responses of neurons in a convergent area. Systematic changes in the proportion of extrinsic projections, the proportion of intrinsic connections, or the amount of local inhibitory contacts affected the multisensory properties of neurons in the convergent area by influencing (1) the proportion of multisensory neurons generated, (2) the proportion of neurons that generate integrated multisensory responses, and (3) the magnitude of multisensory integration. These simulations provide insight into the connectional parameters of convergence that contribute to the generation of populations of multisensory neurons in different neural regions as well as indicate that the simple effect of multisensory convergence is sufficient to generate multisensory properties like those of biological multisensory neurons.

Knowledge discovery approach to automated cardiac SPECT diagnosis.

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.

Synthesis of feedforward networks in supremum error bound.

The main result of this paper is a constructive proof of a formula for the upper bound of the approximation error in Linfinity (supremum norm) of multidimensional functions by feedforward networks with one hidden layer of sigmoidal units and a linear output. This result is applied to formulate a new method of neural-network synthesis. The result can also be used to estimate complexity of the maximum-error network and/or to initialize that network weights. An example of the network synthesis is given.

Solving graph algorithms with networks of spiking neurons.

Spatio-temporal coding that combines spatial constraints with temporal sequencing is of great interest to brain-like circuit modelers. In this paper we present some new ideas of how these types of circuits can self-organize. We introduce a temporal correlation rule based on the time difference between the firings of neurons.With the aid of this rule we show an analogy between a graph and a network of spiking neurons. The shortest path, clustering based on the nearest neighbor, and the minimal spanning tree algorithms are solved using the proposed approach.

A neuro-fuzzy algorithm for diagnosis of coronary artery stenosis.

In this paper a method of fuzzy decision making applied to diagnosis of coronary artery stenosis is presented. The method uses a neural network approach for the diagnosis of stenosis in the three main coronary arteries (left anterior descending, right coronary artery, and circumflex). First, the knowledge base domain, 201Tl scintigram training data, is explained and the method of preprocessing the original heart images is given. Next, the method of dealing with the uncertainties present in the data using the fuzzy approach is outlined. Finally, the algorithm and the results are discussed and compared with other approaches.

A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm.

The relation between the decision trees generated by a machine learning algorithm and the hidden layers of a neural network is described. A continuous ID3 algorithm is proposed that converts decision trees into hidden layers. The algorithm allows self-generation of a feedforward neural network architecture. In addition, it allows interpretation of the knowledge embedded in the generated connections and weights. A fast simulated annealing strategy, known as Cauchy training, is incorporated into the algorithm to escape from local minima. The performance of the algorithm is analyzed on spiral data.

An expert system for diagnosis of coronary artery stenosis based on 201Tl scintigrams using the Dempster-Shafer theory of evidence.

An expert system for the diagnosis of stenoses in the three main coronary arteries (left anterior descending, right coronary artery and circumflex) is described. First, the knowledge base domain--201Tl scintigrams--is explained and the method of preprocessing the original heart images is given. Next, the method of dealing with the uncertainties present both in the cardiologist-specified rules and the data using the Dempster-Shafer theory of evidence is explained. Finally, the constructed expert system and the results are discussed and several graphical examples are shown.

An edge extraction technique for noisy images.

We present an algorithm for extracting edges from noisy images. Our method uses an unsupervised learning approach for local threshold computation by means of Pearson's method for mixture density identification. We tested the technique by applying it to computer-generated images corrupted with artificial noise and to an actual Thallium-201 heart image and it is shown that the technique has potential use for noisy images.

Use of neural networks in detecting cardiac diseases from echocardiographic images.

The usefulness of backpropagation neural networks in the analysis of two-dimensional echocardiographic (2DE) images has been evaluated. The gray-scale levels from 2DE images directly correspond to the intensity of echo signal from cardiac tissue, providing visual texture and allowing qualitative and quantitative analysis of myocardial tissue. A subject population consisting of 11 normal, 7 hypertrophic cardiomyopathy, and 11 myocardial infarction patients was studied. Two types of backpropagational neural networks were used: fully connected, and patterned. In the fully connected network, the outputs of neurodes in each layer are connected to the inputs of all neurodes in the following layer. In the patterned network, only neurodes within a defined neighborhood are connected. The results suggest that the fully connected network provides better classifying performance than the patterned network.

A Bayesian approach for dealing with uncertainties in detection of coronary artery stenosis using a knowledge-based system.

A knowledge-based system that combines subjective Bayesian methods with rules specified by cardiologists to diagnose coronary artery stenosis from postexercise myocardial perfusion scintigrams is discussed. This expert system was used to determine which of the three main coronary arteries had the dominant stenosis. The system also indicated when a patient had a normal myocardial perfusion pattern (no stenosis). The system was run on a set of scans from 91 patients, and the results were compared with an existing expert system that uses the Dempster-Shafer theory of evidence for dealing with uncertainties. The system was able to determine the coronary artery with the dominant stenosis over 90% of the time when supplied with prior knowledge that all the patients have single-vessel stenosis. The system was also able to determine with good accuracy whether a patient had a stenosed coronary artery or normal myocardial perfusion when no prior information was available. The program can be used initially to screen out patients with normal scintigrams. Once the patients with normal scintigrams have been removed, the expert system can then be run on the remaining patients and utilize prior knowledge that they have stenosed coronary arteries. This improves the reliability of the diagnosis.