Enhancing Spatial Pooler Performance in Hierarchical TemporalMemory Algorithm through Sparsification Analysis: An Information TheoryPerspective

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

کلیدواژه ها:

Spatial Pooler (SP) ، Hierarchical Temporal Memory (HTM) ، Sparsity ، Fishery informationmatrix (FIM) ، Cramer-Rao Lower Bound (CRLB)