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Data
Clustering With Spiking Neurons
The
goal of this research is to demonstrate how a system of integrate and fire
(I&F) neurons can be used to perform a clustering task, relying on the fact
that coupled I&F neural systems can exhibit staggered oscillations of
neuronal cell assemblies.
Project
Outcomes
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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.
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The
participating students will be introduced to team-based and
cross-disciplinary research.
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To
help students prepare for graduate school and define career goals and to
encourage them to pursue careers as research scientists.
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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
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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.
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Design
of new spiking neurons based clustering procedures, which incorporate
suitable metrics defining the distance functions for Euclidean and non-
Euclidean feature space.
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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.
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The
results will be submitted for possible presentation and publication at
national conferences.
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