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{\Large
  The Biomedical Engineering Program\\
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  University of Nevada, Reno
}

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{\large cordially invites you to a PhD defense}

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{\huge \bf 
Computational Neuroscience: \\ 
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Theory, Development and Applications in \\
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Modeling The Basal Ganglia
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		A colloquia submitted in partial fulfillment of the\\
        requirements for the degree of Doctor of Philosophy \\
        with a major in Biomedical Engineering.
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by

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{\LARGE Corey M. Thibeault}

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\noindent
{\bf Abstract:}
{

Inspiring entire computing paradigms, hardware platforms and theories of nervous system
function, the field of computational neuroscience has grown steadily since its emergence
in the mid-1980s. The motivation behind it is to mathematically describe the nervous system
in terms of how the structures process information. Simulating the brain this way can
be done at varying levels of abstraction and biological realism; providing insight into the
function of the nervous system or supporting empirical evidence. This dissertation presents
a snapshot of the computational neuroscience landscape. It begins begins with the mathematical
theory, moving to implementation, and finally ending with its application. It is
by no means a complete picture but provides a basic understanding of how mathematical
modeling contributes to neuroscience.
This begins by presenting the design considerations behind high-performance neural
simulation environments. A concept which is then extended with a novel implementation
for the exchange of spiking information in high-performance cluster environments. A
framework for creating virtual environments for embodied modeling is then developed and
discussed. Finally, a toolkit for efficiently analyzing the large amounts of data generated
by these spiking models is presented.
Once these tools are established the focus is shifted to models of the basal ganglia.
After a brief background, spiking models capable of action-selection through reinforcement 
learning are described. These borrow from the basal ganglia but are developed for
implementation on neuromorphic hardware and are therefore necessarily simplified. The
networks are embodied in virtual environments and their performance based on two tasks is
explored under varying conditions. Finally, the use of a simple hybrid neuron is explored in
several published models of the basal ganglia; demonstrating the first example of a hybrid
neuron in biologically faithful models of the basal ganglia


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{\Large \bf 12:00 pm, Wednesday, December 12, 2012}\\
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{\Large Scrugham Engineering and Mines (SEM) room 201}

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{\large
  For more information contact
  Dr. Fred Harris @ 784-6571 (Fred.Harris@cse.unr.edu)
}


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