Hierarchical temporal memory (HTM) provides a theoretical framework that models several
key computational principles of the neocortex. In this paper we analyze an important …

Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence
technology that mimics the architecture and processes of the neocortex. In this white paper …

Empirical evidence demonstrates that every region of the neocortex represents information
using sparse activity patterns. This paper examines Sparse Distributed Representations …

Sparse coding is a basic task in many fields including signal processing, neuroscience and
machine learning where the goal is to learn a basis that enables a sparse representation of …

No generic function for the minicolumn—ie, one that would apply equally well to all cortical
areas and species—has yet been proposed. I propose that the minicolumn does have a …

Mammalian brains consist of billions of neurons, each capable of independent electrical
activity. Information in the brain is represented by the pattern of activation of this large neural …

Hierarchical Temporal Memory (HTM) is a computational theory of machine intelligence
based on a detailed study of the neocortex. The Heidelberg Neuromorphic Computing …

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive
fields. One approach to understanding the emergence of this response is to state that neural …

We consider the problem of learning an unknown (overcomplete) basis from data that are
generated from unknown and sparse linear combinations. Introducing the Sparse Coding …

While evidence indicates that neural systems may be employing sparse approximations to
represent sensed stimuli, the mechanisms underlying this ability are not understood. We …