The Hierarchical Map Forming Model
Date Issued
2006
Date
2006
Author(s)
Soto, Luis Eduardo Rodriguez
DOI
en-US
Abstract
In this thesis we propose a motor control model inspired by organizational priciples of the cerebral cortex. Specifically the model is based on cortical maps and functional hierarchy in sensory and motor areas of the brain. We introduce observed properties of the F5 area in the macaque monkey brain, an area which combines sensory and motor information, producing actions without high processing information. The properties here observed can be quickly summarized to mdularity and hierarchical processing. These form the basis for the model we propose. We make use of well known computational tools, to put
together a biology imitating model, for action learning and motor control. The Self-Organizing Maps (SOM) have proven to be useful in modeling cortical topological maps. A hierarchical SOM provides a natural way to extract
hierarchical information from the environment, which we propose may in turn be used to select actions hierarchically. We use a neighborhood update version of
the Q-learning algorithm, so the final model maps a continuous input space to a continuous action space in a hierarchical, topology preserving manner. The model is called the Hierarchical Map Forming model (HMF) due to the way in which it forms maps in both the input and output spaces in a hierarchical manner.
Subjects
階層控制
類神經網路
Hierarchical Control
Neural Networks
Reinforcement Learning
Type
thesis
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