Abstract
This chapter presents the general background information about the Hierarchical Temporal Memory (HTM). HTM is a recently proposed cognitive learning algorithm that is intended to emulate the overall structural and functionality of the human neocortex responsible for the high-order functions such as cognition, learning and making predictions. The main properties of HTM is hierarchical structure, sparsity and modularity. HTM consists of two main parts: HTM Spatial Pooler (SP) and HTM Temporal Memory (TM). The HTM SP performs the encoding of the input data and produces sparse distributed representation (SDR) of the input pattern useful for visual data processing and classification tasks. The HTM TM detects the temporal changes in the input data and performs prediction making.
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Chapter Highlights
Chapter Highlights
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HTM is a recently proposed cognitive learning algorithm that is intended to emulate the overall structural and functionality of the human neocortex responsible for the high-order functions such as cognition, learning and making predictions.
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The main properties of HTM is hierarchical structure, sparsity and modularity.
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HTM consists of two main parts: HTM Spatial Pooler (SP) and HTM Temporal Memory (TM). The HTM SP performs the encoding of the input data and produces sparse distributed representation (SDR); while the HTM TM detects the temporal changes in the input data and performs prediction making.
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HTM algorithm showed the promising results in various tasks, especially for visual data processing and classification, such as handwritten digits, face and speech recognition.
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There are several hardware implementations of HTM; however, it is still an open problem.
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Dauletkhanuly, Y., Krestinskaya, O., James, A.P. (2020). HTM Theory. 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_13
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DOI: https://doi.org/10.1007/978-3-030-14524-8_13
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