  Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates
sparsity, hierarchy and modularity resembling the working principles of
neocortex. Feature encoding is an important step to create sparse binary
patterns. This sparsity is introduced by the binary weights and random weight
assignment in the initialization stage of the HTM. We propose the alternative
deterministic method for the HTM initialization stage, which connects the HTM
weights to the input data and preserves natural sparsity of the input
information. Further, we introduce the hardware implementation of the
deterministic approach and compare it to the traditional HTM and existing
hardware implementation. We test the proposed approach on the face recognition
problem and show that it outperforms the conventional HTM approach.
