PROJECT GOALS

     Analysis of large sets of data is an important task in any scientific research areas. This unavoidably involves clustering [1], which becomes a difficult task when the data space is high dimensional, ie., when the number of features that characterize each point is very large. The goal of clustering analysis is usually to obtain a symbolic description of the problem and from this an identification procedure. In such situations, it might be useful to rely solely on the distances between the data points. This is the basis of many clustering methods that use these distances as a dissimilarity measure (resemblance or dissemblance). Under the assumption that points within a cluster are closer to one another than two points in other clusters, clustering becomes equivalent to searching for groups of points where the distances between each group cluster, are much smaller than the distances between points in different clusters. Special attention is given to the dynamic clustering methods, which handle adaptively a partition in clusters and a set of symbolic representations of the cluster.

Project Outcomes

• To increase the confidence of women students and to help create a positive and empowering environment for women where they can continue to grow both professionally and personally.
• The students will extend their mathematical background, enhance their computing skills and expertise by introducing them to state-of-the-art hardware and software in object-oriented technology, simulation and modeling.
• The participating students will be introduced to team-based and cross-disciplinary research.
• To help students prepare for graduate school and define career goals and to encourage them to pursue careers as research scientists.
• To perform experiments with I&F neuron models in order to study the how the choice of parameters affect the shape of the spike gain function
• To perform experiments with numerical methods and multi-compartment models in order to study the functional connection between the geometry of the neuron and the activity of the channels. The experimental analysis will help to understand the role of neuron geometrical complexity in synaptic integration.
• Design of new spiking neurons based clustering procedures, which incorporate suitable metrics defining the distance functions for Euclidean and non- Euclidean feature space.
• As for the most of the application problems, the clustering solution might not be unique, we will include a mechanism based on several different approaches, which will recommend the best classification for such cases.
• The results will be submitted for possible presentation and publication at national conferences.