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Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulatessparsity, hierarchy and modularity resembling the working principles ofneocortex. Feature encoding is an important step to create sparse binarypatterns. This sparsity is introduced by the binary weights and random weightassignment in the initialization stage of the HTM. We propose the alternativedeterministic method for the HTM initialization stage, which connects the HTMweights to the input data and preserves natural sparsity of the inputinformation. Further, we introduce the hardware implementation of thedeterministic approach and compare it to the traditional HTM and existinghardware implementation. We test the proposed approach on the face recognitionproblem and show that it outperforms the conventional HTM approach.

Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory