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Biomedical subjects

Y Salu

Publications and source records attributed to Y Salu.

13 recordsLinked to original sources
PubMed1990

An improved method for localizing electric brain dipoles.

Methods for localizing electrical dipolar sources in the brain differ from one another by the models they use to represent the head, the specific formulas used in the calculation of the scalp potentials, the way that the reference electrode is treated, and by the algorithm employed to find the least-squares fit between the measured and calculated EEG potentials. The model presented here is based on some of the most advanced features found in other models, and on some improvements. The head is represented by a three-layer spherical model. The potential on any point on the scalp due to any source is found by a closed formula, which is not based on matrix rotations. The formulas will accept any surface electrode as the reference electrode. The least-squares procedure is based on optimal dipoles, reducing the number of unknowns in the iterations from six to three. The new method was evaluated by localizing five implanted dipolar sources in human sensorimotor cortex. The distances between the locations of the sources as calculated by the method, and the actual locations were between 0.4 and 2.0 cm. The sensitivity of the method to uncertainties encountered whenever a real head has to be modeled by a three-layer model has also been assessed.

Adult↗
PubMed1988

Models of neural novelty detectors, with similarities to cerebral cortex.

A novelty detector is a functional unit, that indicates whether an incoming stimulus is familiar or novel. Novelty detection is prevalent in the central nervous system (CNS), and is involved in various activities. Its basic characteristics are discussed first. Then, models of neural novelty detectors are described, and tested and evaluated in simulations. The simulations have shown that one novelty detector, the bi-compartmental, simulates very closely the behavior of neural novelty detectors. This model is constructed in a way that resembles the observed architecture and function of area 17, and similar regions in the cortex. The first step in novelty detection is data retrieval. The proposed novelty detectors can utilize various compatible modes of data storage and retrieval, and one of those has been utilized in the simulations.

Cerebral Cortex↗
PubMed1985

Learning and coding of concepts in neural networks.

Our environment consists of virtually an infinite number of scenarios in which we have to function. In order to respond properly to an incoming stimulus, the brain has first to analyze it, and to find out the basic familiar elements that are part of it. In other words, by using a library which contains a relatively small number of basic concepts, the brain analyzes the multitude of incoming events. Some of those basic concepts are innate, but many of them must be learned, in order to accommodate for the arbitrary environment around us. A classifying box is defined as the neural network that finds out the familiar concepts that are present in an incoming stimulus. Models for classifying boxes are introduced, and possible mechanisms by which they may establish their libraries of concepts are suggested, and then compared and evaluated by computer simulations.

Classification↗
PubMed1984

Theoretical models and computer simulations of neural learning systems.

It has been generally assumed for a long time that learning is accomplished in the central nervous system (CNS) by modifying strengths of ties between neurons. Various mechanisms may contribute to this process, but it is not known which are the specific mechanisms, and what are the rules by which they operate. Theoretical models, which are based on that general assumption are introduced. The purpose of the models is to suggest plausible ways by which learned information may be stored in the neural network, and be retrieved when it is needed. The networks in the models consist of four basic subunits, in accordance with identified units in the CNS: sensing, response, feeling, and control, plus association areas. The suggested operation rules are based on established operation rules of individual neurons, and assumed rules when neurons in groups are considered. Computer simulations are done, to check the consistency of the models, and to illustrate how they work. They simulate how an hypothetical kitten learns part of its environment, and show how relevant information may be stored and retrieved in its neuronal network. The suggested mechanisms could be examined in experiments, albeit not easy ones to conduct.

Animals↗
PubMed1984

A computerized system for localizing sources of cardiac activation.

A noninvasive method for locating a source of cardiac electrical activity is described. The data acquisition and its preliminary processing is done with the aid of a microcomputer, while lengthier calculations are done on a large computer. The method was tested on 18 patients, and the results indicate that it is reliable, and with further technical refinements it could be used in research and clinical settings.

Arrhythmias, Cardiac↗
PubMed1983

Computer simulations of learning in neural systems.

Recent experiments have shown that, in some cases, strengths of synaptic ties are being modified in learning. However, it is not known what the rules that control those modifications are, especially what determines which synapses will be modified and which will remain unchanged during a learning episode. Two postulated rules that may solve that problem are introduced. To check their effectiveness, the rules are tested in many computer models that simulate learning in neural systems. The simulations demonstrate that, theoretically, the two postulated rules are effective in organizing the synaptic changes. If they are found to also exist in biological systems, these postulated rules may be an important element in the learning process.

Animals↗
PubMed1982

A noninvasive method for locating a cardiac dipolar source in humans.

A noninvasive method for locating isolated areas of cardiac electrical activity, such as an ectopic focus, is introduced and evaluated. Surface electric potentials due to the source are recorded at 20-25 points on the chest. Each chest is measured and its configuration is approximated by a grid of 126 points. A computer program utilizes these data to calculate the x, y, z coordinates of the cardiac source, and this calculated location is displayed on the bi-plane chest x-rays of the patient. Errors that may occur in other methods that utilize average torso shape and average heart position are eliminated in the proposed method. The method was evaluated by using it for locating the electrodes of implanted pacemakers from their "spikes" in the ECG's, and also from the potentials at the onset of the induced QRS complexes. The results were compared with bi-plane x-rays of the same patients. It was found that the average errors in locating the electrodes from their "spikes" in the ECG's were 1.3 cm in the frontal view and 1.4 cm in the lateral view. The errors in locating the paced myocardial area at the beginning of the QRS complex were similar. The errors in the lateral view are systematic and may be attributed to the effects of the intracavity blood volume (Brody's effect), which are neglected in this method.

Computers↗
PubMed1978

Effects of the volume conductor on the apparent orientation of a known cardiac dipole.

The surface electrocardiogram (EKG) is dependent on two major factors: the cardiac generator and the volume conductor. This investigation assessed the effects of the volume conductor in man on the apparent orientation of a simulated cardiac dipole. The apparent orientation of the dipole was calculated from measured surface potentials from about 60 locations on the body of five patients with implanted cardiac pacemakers. The real orientation of the dipole (an implanted pacemaker) was determined radiographically. The effects of both inhomogeneity and boundary characteristics of the volume conductor on the apparent orientation of the dipole were assessed using a new inverse algorithm. The difference between the orientation of the real and the calculated dipoles averaged 30 degrees (range 15 degrees--40 degrees) when the torso was assumed to be an infinite-homogeneous volume conductor. When the configuration of the torso was accounted for, however, the difference between the orientation of the real and calculated dipoles was reduced to 9 degrees (range 5 degrees--13 degrees). Thus, by taking into account the geometry of the torso and neglecting the inhomogeneities in the volume conductor, it is possible to calculate the orientation of a dipole in the cardiac region with an accuracy of about 9 degrees. It is reasonable to assume that the orientation of real activation wave fronts from localized areas of the heart could be calculated with a similar degree of accuracy.

Aged↗
PubMed1976

Computer simulation of the precordial QRS complex: effects of simulated changes in ventricular wall thickness and volume.

The cardiac electric field generated by depolarization of the human ventricle is simulated with a computer model which utilizes 1,500 dipoles. The configuration of the ventricles utilized in the model assumed that the cross-sectional shape of the left ventricle was circular and the right ventricular free wall was a portion of an ellipse. The torso was assumed to be homogeneous and infinite. The activation sequence was based on the measurements of Durrer. The depolarizational wave was simulated by dipole layers. The output of the model is presented as a standard multilead precordial ECG. The ECG complexes generated by the model closely resemble the precordial QRS complexes of normal man. Simulated increases in wall thickness (1 to 2.2 X control) were associated with changes in the calculated precordial QRS complexes which were characteristic of left ventricular hypertrophy. Voltage (R in V5 or V6 and S in V1) and QRS duration increased linearly as a function of calculated left ventricular mass. Increases in ventricular activation time were related nonlinearly to changes in left ventricular mass and did not occur in the absence of a simulated increase in wall thickness. The effects of simulated changes in left ventricular volume (0.6 to 3.0 X control) on the QRS complex were mainly dependent on the resultant increase in left ventricular mass. This model may be useful in simulating the precordial QRS complexes that result from isolated or combined changes in ventricular volume or wall thickness or other disorders of the heart. Furthermore, it may be useful whenever a simulation of a QRS generator is needed.

Cardiac Volume↗