Request Confirmation Networks: A Cortically Inspired Approach to Neuro-Symbolic Script Execution

Biomimetic systems;Machine learning

Gallagher, Katherine

University:Havard University

Department:Software Engineering

关键词: Biomimetic systems;    Machine learning;   

Others  :  https://dash.harvard.edu/bitstream/handle/1/37364547/GALLAGHER-DOCUMENT-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: Digital Access to Scholarship at Harvard

【 摘 要 】

This thesis examines Request Confirmation Networks (ReCoNs), hierarchical spreading activation networks with constrained top-down/bottom-up recurrency that are proposed as a possible model for cortical activity during execution of neuro-symbolic sensorimotor scripts. ReCoNs are evaluated in the context of the Function Approximator, a showcase implementation that calculates a function value from a handwritten image of the function. Background is provided on biological and artificial neural networks, with emphasis on other biomimetic approaches to machine learning.