George Georgiev

Modeling weakly connected networks of neural oscillators with spiking neurons

The goal of this research is to investigate the relationships between synaptic organizations (anatomy) of the neural networks and the dynamical properties (function) of weakly connected networks of neural oscillators. It is shown how certain parameters of the spiking neuron model can be used to represent these dynamics. The two proposed models are based on the two main cell types in the olfactory bulb, the mitral and granule cells. The dynamics that have been simulated include the reciprocal and lateral inhibition of mitral cells by granule cells, as well as the saturation of mitral cells. The simulations show how certain spike inputs to mitral cells correspond to cortex recognition and discrimination in the olfactory bulb.

QRS Complex Detector Implementing Orthonormal Functions

Abstract Heart is one of the most important organs in the human body and disorders in its functioning can cause serious problems. Arrhythmias are abnormal heart beats. In fact, arrhythmias are heart diseases, caused by heart electrical-conductive system disorders. They are characterized with very slow (bradycardia) or very fast (tachycardia) heart functions resulting in an inefficient pumping. The heart state is generally reflected in the shape of ECG waveform and heart rate. Various computer-based methodologies for automatic diagnosis have been proposed by researchers; however the entire process can generally be subdivided into a number of separate processing modules such as preprocessing, feature extraction/selection, and classification. In this research we focus on filtering the ECG signal in order to remove high frequency noise and enhance the QRS complexes, and on feature extraction. The latter is the determination of a feature vector from the ECG pattern vector. Our feature selection approach is based on implementation of orthonormal functions. Representing ECG morphology with coefficients of orthonormal polynomials results in robust estimates of a few descriptive signal parameters. Exposition of subtle features of normal and deviating ECG pattern vectors allows their accurate representation. The experimental data includes recordings from MIT dataset.

Odor information processing in human-oscillatory model

Our goal is to simulate the dynamic behavior of the olfactory bulb as part of the olfactory system in order to study how the olfactory neural system processes the odorant molecular information. We base our model on coupled nonlinear oscillators, which resemble groups of mitral and granule cells as main building units. Odors evoke oscillatory activity in populations of excitatory and inhibitory cells of the proposed two-layer architecture with feedforward and feedback connections between them, and the system exhibits odor-specific adaptation. The output of a unit, representing the average firing rate within the corresponding population, is modeled as a sigmoidal function of the synaptic input. The system exhibits complex oscillatory behavior, simulating mammalian olfactory bulb. Simulations show that the dynamic behavior of the model is stable under the influence of noise. It is shown that the simulations depend on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. The model is able to reduce noise, allowing the pattern to emerge from incomplete and noisy input.

Simulating Voltage-Gated Na and K Ion Channel Kinetics Using Hodgkin- Huxley Mode

Abstract Voltage-gated sodium channels play an important role in action potentials. If enough channels open during a change in the cells membrane potential, a small but significant number of sodium ( Na + ) ions will move into the cell reducing its electrochemical gradient and further depolarizing the cell. Voltage-gated Na + channels play a fundamental role in the excitability of nerve and muscle cells. Na + channels both open and close more quickly than potassium ( K + ) channels, producing an influx of positive charge ( Na + ) toward the beginning of the action potential and an efflux ( K + ) toward the end. The study of K + channels is essential as they appear to be more diverse in structure and function than any other types of ion channel. K + channels shape the action potential, set the membrane potential, and determine firing rates. There already are some drugs in clinical use that target K + channels which improve our ability to regulate excitability. In this research, we study the influence of voltage dependence on channel activation and inactivation by simulating different channel subtypes as well as the effect of different kinetic parameters on membrane excitability.

Binary Tree Classifier Based on Kolmogorov-Smirnov Test

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data causes a significant computational problem. Decision, tree classification is a popular approach to the problem and an efficient form for representing a decision process in hierarchical pattern recognition systems. They are characterized by the property that samples are subjected to a sequence of decision rules before they assigned to a unique class.

Growing radial basis neural networks with potential function generators

In this paper, we propose an approach for shaping the adaptive radial basis functions through potential functions for the purposes of classification. We propose a multilayer potential function generators neural network (PFUGNN) with two fundamental components: potential function generators (PFGs) and potential function entities (PFEs) which create the decision rules by constructing multivariate potential functions and adjusting the weights as well as the parameters of the cumulative potential functions. The two proposed criteria evaluate the NN performance during the learning phase and force PFUGNN to enter the dynamic phase and perform structural changes before entering the next learning cycle. The implementation of the presented method with several data sets demonstrates its power in generating classification solutions for learning samples of various shapes.

Learning and knowledge extraction from a potential based neural network

In this paper, we present a strategy of shape-adaptive radial basis functions (RBF) based on potential functions. We also propose a neural network topology, which is based on RBFs and synthesized potential fields. The originality of the presented approach is in the training algorithm, which sequentially adds basis functions (centered on training data points) if this improves the classification performance. The experiments with several datasets demonstrate the algorithms power in generating classification solutions for learning samples of various shapes. We discuss the implementation of the presented method with two large data sets (vehicle silhouettes and shuttle control sets). We compare the classification performance on the training and test sets achieved by the proposed approach and some other neural network models.

Synthesis of models for receptive field dynamics and synaptic transmission using spiking neurons

The aim of this paper is to show the way in which the odor is represented, and how the receptors would react to samples of odors. Sequence of spikes generated by a neuron is completely characterized by the neural response function. Spiking models involve dynamics over time scales ranging from channel openings that can take less than a millisecond, to collective neural network processes that may be several orders of magnitude slower. The tuning specification between the mitral and tufted cells and their glomeruli plays a vital role in mitral and tufted cells spike response to a particular odor. The experiments indicate that a single mitral /tufted cell show excitatory spike response to a range of odor molecules with similar molecular configuration. The behavior of the receptor cells in response to the simulated odors is close to the actual biological behavior of the receptors present in the olfactory epithelium.