Brain modeling and simulations

The understanding of the nonlinear dynamics of the Olfactory Bulb (OB) is essential to the modeling of the brain and nervous system. On the base of our study of the OB activities and the analysis of the conditions governing neural oscillations and the nature of odor-receptor interactions, we proposed and developed models and simulations addressing the questions of how the brain recognizes odors, how it works in a noisy natural environment, and why synchronization is used for decoding brain circuits, which are still not successfully solved.  We simulated the dynamic behavior of the olfactory system in order to understand the way in which odors are represented and processed by the brain. Further experiments with artificial olfactory systems (AOS) based on different sets of parameters, will allow the simulation and study how neuropathological changes appear in cortex which receives projections from the olfactory bulb.  Odor threshold, odor identification, detection and recognition are the basic measures of the medical studies detection and diagnosis of neurodegenerative diseases such as, Alzheimer’s, Parkinson’s disease, and schizophrenia.

Self-organizing maps

Self-Organizing Maps (SOM) have become quite popular for tasks in data visualization, pattern classification or natural language processing and can be seen as one of the major concepts for artificial neural networks of today. Their general idea is it to approximate a high dimensional previously unknown input distribution by a lower dimensional neural network structure with the goal to model the topology of the input space as well as possible. Classical Self-Organizing Maps read the input values in random but sequential order one by one and thus adjust the network structure over space: The network will be built while reading bigger and bigger parts of the input. In contrast to this approach, we present a Self-Organizing Map that processes the whole input in parallel and organizes itself over time. The main reason for parallel input processing lies in the fact that knowledge can be used to recognize parts of patterns in the input space that have already been learned. This way, networks can be developed that don’t reorganize their structure from scratch every time a new set of input vectors is presented but rather adjust their internal architecture in accordance with previous mappings. One basic application could be a modeling of the whole-part relationship through layered architectures.

Radial basis function neural networks

The learning strategy used in RBFNs consists of approximating an unknown function with a linear combination of non-linear functions called basis functions. The latter have radial symmetry with respect to a center. A new strategy of shape-adaptive radial basis functions based on potential functions and optimization procedure for positioning of the centers during the learning process is explored. Proposed are static and dynamic versions of our learning algorithm. The approach is distinctive from the conventional approaches using space segmentation or neural network energy convergence.

Medical image processing

The basic idea is the segmentation and classification of magnetic resonance images (MRI) of the brain. The purpose is the segment the white matter in the brain from the gray matter. The white matter contains information about the blood flow in the brain, which is then used along with positron image tomography images as possible diagnostic tool. The process of segmentation is a very complex one, due to non-uniformity of the MRI. The human experts have long been the most reliable segmentors. The aim is to create an intelligent aide in the form of a neural network.