Analysis framework for adaptive spiking neural networks
Wang, Felix
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https://hdl.handle.net/2142/50483
Description
Title
Analysis framework for adaptive spiking neural networks
Author(s)
Wang, Felix
Issue Date
2014-09-16
Director of Research (if dissertation) or Advisor (if thesis)
Levinson, Stephen E.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Spiking Neural Network
Phenomenological models
Bottom-up
Learning
Adaptation
Closed-loop
Parallel
Asynchronous
Simulation
Abstract
Learning is an inherently closed-loop process that involves the interaction between an intelligent agent and its environment. In the human brain, we assert that the basis for learning is in its ability to represent external stimuli symbolically in an associative memory. Historically, statistical methods such as the hidden Markov model have been used in order to provide the internal symbolic representation to external signals from the environment. This work approaches similar themes by investigating the function of the neocortex, with the ultimate goal of understanding how mental states might arise from spiking activity. Cortical modeling has traditionally focused on the mechanisms and behaviors at the cellular level. However, developments with respect to group or population level phenomena indicate that a shift in focus is necessary to understand how learning and representation of stimuli might occur in the brain. We present a Simulation Tool for Asynchronous Cortical Streams (STACS) for studying spiking neural networks exhibiting adaptation in a closed-loop system.
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