Title :
Multi-layer Cortical Learning Algorithms
Author :
Agrawal, Pulin ; Franklin, Stan
Author_Institution :
Dept. of Comput. Sci., Univ. of Memphis, Memphis, TN, USA
Abstract :
Hierarchical Temporal Memory (HTM) is a model with hierarchically connected modules doing spatial and temporal pattern recognition, as described by Jeff Hawkins in his book entitled On Intelligence. Cortical Learning Algorithms (CLAs) comprise the second implementation of HTM. CLAs are an attempt by Numenta Inc. to create a computational model of perceptual analysis and learning inspired by the neocortex in the brain. In its current state only an implementation of one isolated region has been completed. The goal of this paper is to test if adding a second higher level region implementing CLAs to a system with just one region of CLAs, helps in improving the prediction accuracy of the system. The LIDA model (Learning Intelligent Distribution Agent - LIDA is a cognitive architecture) can use such a hierarchical implementation of CLAs for its Perceptual Associative Memory.
Keywords :
cognitive systems; content-addressable storage; learning (artificial intelligence); multi-agent systems; pattern recognition; CLA; HTM; LIDA model; On Intelligence; brain; cognitive architecture; computational model; hierarchical temporal memory; hierarchically connected modules; learning intelligent distribution agent; multilayer cortical learning algorithm; neocortex; perceptual analysis; perceptual associative memory; second higher level region; spatial pattern recognition; temporal pattern recognition; Accuracy; Bayes methods; Predictive coding; Predictive models; Standards; Training; Vectors; Cortical Learning Algorithms; LIDA; feedback message; pattern recognition; predictive coding;
Conference_Titel :
Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CCMB.2014.7020707