چکیده مقاله
Hierarchical Temporal Memory HTM is an unsupervised machine learning algorithm inspired byneocortical computational principles The Spatial Pooler SP , a core component of HTM, converts binaryinput into sparse distributed representations This study examines SP's sparsification through aninformation theory perspective, demonstrating that increased sparsity enhances SP's performance Comparative analyses using Gaussian and non Gaussian e g , Cauchy distribution data distributionsreveal that sparsity levels significantly impact SP's output, as assessed by the Cramer–Rao lower bound Our findings highlight the critical role of sparsity in optimizing SP's performance and offer insights forthe design and optimization of HTM algorithms
کلیدواژهها
نویسندگان
Shiva Sanati
Dept of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Modjtaba Rouhani
Dept of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Ghosheh Abed Hodtani
Dept of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
شیوه ارجاع
Sanati, Shiva and Rouhani, Modjtaba and Abed Hodtani, Ghosheh,1403,Enhancing Spatial Pooler Performance in Hierarchical TemporalMemory Algorithm through Sparsification Analysis: An Information TheoryPerspective,The 23th National Conference on Computer Science and Engineering and Information Technology,Babol
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