  Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm
that holds promise for invariant representations of spatial and spatiotemporal
inputs. This paper presents a comprehensive neuromemristive crossbar
architecture for the spatial pooler (SP) and the sparse distributed
representation classifier, which are fundamental to the algorithm. There are
several unique features in the proposed architecture that tightly link with the
HTM algorithm. A memristor that is suitable for emulating the HTM synapses is
identified and a new Z-window function is proposed. The architecture exploits
the concept of synthetic synapses to enable potential synapses in the HTM. The
crossbar for the SP avoids dark spots caused by unutilized crossbar regions and
supports rapid on-chip training within 2 clock cycles. This research also
leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic
plasticity to strengthen the robustness and performance of the SP. The proposed
design is benchmarked for image recognition tasks using MNIST and Yale faces
datasets, and is evaluated using different metrics including entropy,
sparseness, and noise robustness. Detailed power analysis at different stages
of the SP operations is performed to demonstrate the suitability for mobile
platforms.
