Abstract
This chapter covers the memristive HTM implementations on mixed-signal and analog hardware. Most of the implemented memristive systems are based on modified HTM algorithm. The HTM is often used as a feature encoding and feature extraction tool, and these features are then used with conventional nearest neighbor method for classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fan D, Sharad M, Sengupta A, Roy K (2016) Hierarchical temporal memory based on spin-neurons and resistive memory for energy-efficient brain-inspired computing. IEEE Trans Neural Netw Learn Syst 27(9):1907–1919
Hawkins J, George D (2006) Hierarchical temporal memory: Concepts, theory and terminology. Technical report, Numenta
Hawkins J, Ahmad S, Dubinsky D (2010) Hierarchical temporal memory including htm cortical learning algorithms. Technical report, Numenta, Inc, Palto Alto. http://www.numenta.com/htmoverview/education/HTM_CorticalLearningAlgorithms.pdf
Ibrayev T, Krestinskaya O, James AP (2017) Design and implication of a rule based weight sparsity module in htm spatial pooler. In: 2017 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, pp 274–277
Ibrayev T, James AP, Merkel C, Kudithipudi D (2016) A design of htm spatial pooler for face recognition using memristor-cmos hybrid circuits. In: 2016 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, pp 1254–1257
Ibrayev T, Myrzakhan U, Krestinskaya O, Irmanova A, James AP (2018) On-chip face recognition system design with memristive hierarchical temporal memory. J Intell Fuzzy Syst 34(3):1393–1402
Irmanova A, Ibrayev T, James AP (2017) Discrete-level memristive circuits for htm-based spatiotemporal data classification system. IET Cyber Phys Syst Theory Appl 3(1):34–43
James AP, Fedorova I, Ibrayev T, Kudithipudi D (2017) Htm spatial pooler with memristor crossbar circuits for sparse biometric recognition. IEEE Trans Biomed Circuits Syst 11(3):640–651
James A, Ibrayev T, Krestinskaya O, Dolzhikova I (2018) Introduction to memristive htm circuits. In: Memristor and Memristive Neural Networks. InTech
Krestinskaya O, James AP (2018) Feature extraction without learning in an analog spatial pooler memristive-cmos circuit design of hierarchical temporal memory. Analog Integr Circuits Signal Process 95(3):457–465
Krestinskaya O, Dolzhikova I, James AP (2018) Hierarchical temporal memory using memristor networks: a survey. IEEE Trans Emerg Top Comput Intell 2(5):380–395. https://doi.org/10.1109/TETCI.2018.2838124
Krestinskaya O, Ibrayev T, James AP (2018) Hierarchical temporal memory features with memristor logic circuits for pattern recognition. IEEE Trans Comput Aided Des Integr Circuits Syst 37(6):1143–1156
Krestinskaya O, James AP, Chua LO (2018) Neuro-memristive circuits for edge computing: a review. arXiv preprint arXiv:1807.00962
Lazzaro J, Ryckebusch S, Mahowald MA, Mead CA (1989) Winner-take-all networks of o (n) complexity. In: Advances in Neural Information Processing Systems, pp 703–711
Zyarah AM, Kudithipudi D (2015) Reconfigurable hardware architecture of the spatial pooler for hierarchical temporal memory. In: 2015 28th IEEE International System-on-Chip Conference (SOCC). IEEE, pp 143–153
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Chapter Highlights
Chapter Highlights
-
Most of the hardware implementations of HTM with memristive devices are based on the modification of original HTM algorithm.
-
HTM SP can be used for both: original HTM and for the extraction of meaningful features from input data in the traditional nearest neighbor classification approach.
-
Even thought several HTM implementations have been proposed in recent years [1, 8, 12], the full on-chip hardware implementation of original HTM algorithm is an open problem.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Krestinskaya, O., Dolzhikova, I., James, A.P. (2020). Memristive Hierarchical Temporal Memory. In: James, A. (eds) Deep Learning Classifiers with Memristive Networks. Modeling and Optimization in Science and Technologies, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-14524-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-14524-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14522-4
Online ISBN: 978-3-030-14524-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)