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NuPIC Community Content
The following resources have been provided by community members of the NuPIC open source project and others interested in HTM theory.
The goal of this post is (a) to briefly recall what sparse representations are, and (b) how to encode real-world data as sparse representations. We roughly follow Numenta’s approach and connect it to a construction well-known in computational topology called witness complex — a simplicial complex that can be understood as a simplicial approximation of the inputs space.
A Mathematical Formalization of Hierarchical Temporal Memory Cortical Learning Algorithm's Spatial Pooler
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, currently does not have a comprehensive mathematical framework. This work brings together all aspects of the spatial pooler (SP), a critical learning component in HTM, under a single unifying framework. The primary learning mechanism is explored, where a maximum likelihood estimator for determining the degree of permanence update is proposed. The boosting mechanisms are studied and found to be only relevant during the initial few iterations of the network. Observations are made relating HTM to well known algorithms such as competitive learning and attribute bagging. Methods are provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verifies that given the proper parameterizations, the SP may be used for feature learning.
James Mnatzaganian Ernest Fokoué Dhireesha Kudithipudi
NuPIC Studio is an all-in-one tool that allows users create a HTM neural network from scratch, train it, collect statistics, and share it among the members of the community.
David Ragazzi and Team
From a student in Artificial Intelligence with a Neural Network course in fall semester of 2014.
MSc student in Artificial Intelligence
Computer Eng. & Information Tech. Department
Amirkabir University of Technology
Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function
Michael R. Ferrier
Department of Cognitive Linguistic and Psychological Sciences
Master Thesis for the acquisition of the academic degree Master of Science (M.Sc.)
Blog Posts by Felix Andrews on his HTM Implementation in Clojure
Fergal Byrne on HTM and NuPIC
- Real Machine Intelligence (online book, in progress)
- Article by Fergal Byrne with RDSE implementation in Clojure
John Thornton (Griffith University)Papers on HTM by Associate Professor
- Evaluating sparse codes on handwritten digits PDF (Australasian Joint Conference on AI 2013)
- Spatial Pooling for Greyscale Images PDF (International Journal of Machine Learning and Cybernetics - 2013)
- Fixed Frame Temporal Pooling PDF (AI 2012: Advances in Artificial Intelligence, 25th Australasian Joint Conference on Artificial Intelligence, Sydney, Australia, December 4-7, 2012)
- Augmented Spatial Pooling PDF (AI 2011: Advances in Artificial Intelligence, 24th Australasian Joint Conference on Artificial Intelligence, Perth, Australia, December 5-8, 2011)
- Offline Cursive Character Recognition: A state-of-the-art comparison PDF (Proceedings of the 14th Conference of the International Graphonomics Society IGS 2009, Dijon, France, September 13-16, 2009)
- Character Recognition using Hierarchical Vector Quantization and Temporal Pooling PDF (AI 2008: Advances in Artificial Intelligence, 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008)
This talk discusses Tim McNamara's experience with NuPIC for building useful artificial intelligence applications. Presented at the Kiwi Pycon in New Zealand.
A guest talk by Chetan Surpur for MV Neurosciences.
Installing Nupic on Windows, from Aseem Hegshetye.
Detailed document from Christian Cleber Masdeval Braz with wonderful graphics describing the CLA.
Hierarchical Temporal Memory Cortical Learning Algorithm for Pattern Recognition on Multi-core Architectures
By Ryan William Price, Portland State University.