Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/62219
Title: Neural modeling of multiple memory systems and learning
Authors: Wang, Wenwen
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2015
Source: Wang, W. (2015). Neural modeling of multiple memory systems and learning. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This thesis presents a biologically inspired multi-memory system for modeling the structures and connections between the procedural and declarative memories. Using multi-channel self-organizing neural networks as building blocks, the proposed multi-memory system includes a procedural memory model that learns decision through reinforcement learning, an episodic memory model that encodes an individual's experience in the form of events and their spatio-temporal relations, and a semantic memory that captures factual knowledge. We have further proposed two major interaction process between the three memories. We further investigated the overall performance of the memory system on a first person shooting game and a Starcraft Broodwar strategic game. Our experimental results show that the system model is able to learn various forms of knowledge for the different domain tasks. The results also confirms that the memory interaction can lead to a significant improvement in both learning efficiency and performance.
URI: https://hdl.handle.net/10356/62219
DOI: 10.32657/10356/62219
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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