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IEEE Symposium on Computational Intelligence, Cognitive algorithms, Mind, and Brain

作者： Agrawal, Pulin Franklin, Stan Univ Memphis Dept Comp Sci Memphis TN 38152 USA Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA

ISBN: (纸本)9781479945498

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.

关键词： cortical learning algorithms predictive coding LIDA feedback message pattern recognition

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Enhancement of Classifiers in HTM-CLA Using Similarity Evaluation Methods 19th

Enhancement of Classifiers in HTM-CLA Using Similarity Evalu...

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19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES)

作者： Balasubramaniam, Jahan Krishnaa, Gokul C. B. Zhu, Fangming Natl Univ Singapore Inst Syst Sci Singapore 117548 Singapore

The recent development in the theory of Hierarchical Temporal Memory (HTM) - cortical learning algorithms (CLA) which models the structural and algorithmic properties of neocortex has brought in a new paradigm in the field of machine intelligence. As the theory of HTM-CLA continues to evolve, the ways of inferring the patterns and structures recognized by the HTM-CLA algorithm are still a big challenge. Moreover, the existing methods used to infer the classification output from HTM-CLA are far from satisfactory. In this paper, we propose two new classifiers using similarity evaluation methods based on dot similarity (HDS) and mean-shift clustering (H-MSC) to obtain classification from the sparse distributed representation (SDR) output of HTM-CLA. We validate and benchmark the performance of our proposed classifiers using three datasets from the UCI machine learning repository. The results show that the proposed classifiers enhance the classification performance of HTM-CLA and their performance is also comparable to other traditional machine learning technique such as decision tree. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Hierarchical Temporal Memory cortical learning algorithms HTM-CLA Similarity Evaluation Classifier

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Enhancement of Classifiers in HTM-CLA Using Similarity Evaluation Methods

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Procedia Computer Science 2015年 60卷 1516-1523页

作者： Jahan Balasubramaniam C.B. Gokul Krishnaa Fangming Zhu Institute of Systems Science National University of Singapore Singapore

The recent development in the theory of Hierarchical Temporal Memory (HTM) - cortical learning algorithms (CLA) which models the structural and algorithmic properties of neocortex has brought in a new paradigm in the field of machine intelligence. As the theory of HTM-CLA continues to evolve, the ways of inferring the patterns and structures recognized by the HTM-CLA algorithm are still a big challenge. Moreover, the existing methods used to infer the classification output from HTM-CLA are far from satisfactory. In this paper, we propose two new classifiers using similarity evaluation methods based on dot similarity (H-DS) and mean-shift clustering (H-MSC) to obtain classification from the sparse distributed representation (SDR) output of HTM-CLA. We validate and benchmark the performance of our proposed classifiers using three datasets from the UCI machine learning repository. The results show that the proposed classifiers enhance the classification performance of HTM-CLA and their performance is also comparable to other traditional machine learning technique such as decision tree.

关键词： Hierarchical Temporal Memory cortical learning algorithms HTM-CLA Similarity Evaluation Classifier

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Simulating cortical networks on heterogeneous multi-GPU systems

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2013年第7期73卷 953-971页

作者： Nere, Andrew Franey, Sean Hashmi, Atif Lipasti, Mikko Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

Recent advances in neuroscientific understanding have highlighted the highly parallel computation power of the mammalian neocortex. In this paper we describe a GPGPU-accelerated implementation of an intelligent learning model inspired by the structural and functional properties of the neocortex. Furthermore, we consider two inefficiencies inherent to our initial implementation and propose software optimizations to mitigate such problems. Analysis of our application's behavior and performance provides important insights into the GPGPU architecture, including the number of cores, the memory system, atomic operations, and the global thread scheduler. Additionally, we create a runtime profiling tool for the cortical network that proportionally distributes work across the host CPU as well as multiple GPGPUs available to the system. Using the profiling tool with these optimizations on Nvidia's CUDA framework, we achieve up to 60 x speedup over a single-threaded CPU implementation of the model. (c) 2012 Elsevier Inc. All rights reserved.

关键词： cortical learning algorithms CUDA GPGPU Profiling systems

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