The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

ABSTRACT: Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be direc

SUBMITTER: Cui Y 

PROVIDER: S-EPMC5712570 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature