Volume 10 Supplement 1
Affect-driven learning in an avalanche neural network modeling early sensorimotor intelligence
© Hill et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
This paper discusses a key learning element in an agent-based model of early sensorimotor learning during the first month of life. The system is based on the hypothesis that the earliest learned habits are driven by affectivity (innate non-objective preferences) and are grafted onto innate sensorimotor habits. The agent demonstrates the ability for learned associations, gradually overriding innate reflexes and producing learned voluntary motions. Additionally, the agent exhibits learned avoidance behaviors that could override innate affective preferences and reflexes .
The key learning element discussed here is an enhanced three-layer avalanche chain network. Avalanche chain neural network theory  was originally developed by Grossberg to model motor learning based upon a psychological model. He presented a conceptual mathematical schema for these systems. In this work, we report a specific implementation of the system. One finding of this work is that the original 2-level Grossberg topology must be extended to a 3-level system to deal with interruptions and unanticipated changes in the affective state of the agent. Specific sets of mathematical equations implementing robust avalanche chain learning are presented.
This work was supported by the National Science Foundation, award # 0648202.
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This article is published under license to BioMed Central Ltd.