Analysis framework for adaptive spiking neural networks

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Wang, Felix

Contributors dc:contributor

• Levinson, Stephen E.

• Spiking Neural Network
• Phenomenological models
• Bottom-up
• Learning
• Adaptation
• Closed-loop
• Parallel
• Asynchronous
• Simulation

Statement dc:rights

• Copyright 2014 Felix Wang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/50483

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/50483