Parallel Implementation of Spatial Pooler in Hierarchical Temporal Memory

Marcin Pietron, Maciej Wielgosz, Kazimierz Wiatr

Abstract: Hierarchical Temporal Memory is a structure that models some of the structural and algorithmic properties of the neocortex. HTM is a biological model based on the memory-prediction theory of brain. HTM is a method for discovering and learning of observed input patterns and sequences, building an increasingly complex models. HTM combines and extends approaches used in sparse distributed memory, bayesian networks, spatial and temporal clustering algorithms, using a tree-shaped hierarchy neural networks. It is quite a new model of deep learning process, which is very efficient technique in artificial intelligence algorithms. HTM like other deep learning models (Boltzmann machine, deep belief networks etc.) has structure which can be efficiently processed by parallel machines. Modern multi-core processors with wide vector processing units (SSE, AVX), GPGPU are platforms that can tremendously speed up learning, classifying or clustering algorithms based on deep learning models (e.g. Cuda