Vector Symbolic Architectures (VSA) are approaches to representing symbols and structured combinations of symbols as high-dimensional vectors. They have applications in machine learning and for understanding information processing in neurobiology. VSAs are typically described in an abstract mathematical form in terms of vectors and operations on vectors. In this work, we show that a machine learning approach known as hierarchical temporal memory, which is based on the anatomy and function of mammalian neocortex, is inherently capable of supporting important VSA functionality. This follows because the approach learns sequences of semantics-preserving sparse distributed representations.Daniel E. Padilla and Mark D. McDonnel
Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language prese...
Sequence learning, prediction and generation has been proposed to be the universal computation perfo...
Vector-symbolic architectures (VSAs) provide methods for computing which are highly flexible and car...
The field of Artificial Intelligence (AI) has achieved enormous progress in the past decade thanks p...
Vector Symbolic Architectures (VSA) were first proposed as connectionist models for symbolic reasoni...
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despit...
Pattern recognition is an area constantly enlarging its theoretical and practical horizons. Applicat...
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computat...
This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly ...
In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first...
Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article present...
Hierarchical Temporal Memory is a brain inspired memory prediction framework modeled after the unifo...
In this article we show the existence of a formal convergence between the matrix models of biologica...
Abstract. There is increasing evidence to suggest that the neocortex of the mammalian brain does not...
The main focus of this thesis lies in a rather narrow subfield of Artificial Intelligence. As any be...
Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language prese...
Sequence learning, prediction and generation has been proposed to be the universal computation perfo...
Vector-symbolic architectures (VSAs) provide methods for computing which are highly flexible and car...
The field of Artificial Intelligence (AI) has achieved enormous progress in the past decade thanks p...
Vector Symbolic Architectures (VSA) were first proposed as connectionist models for symbolic reasoni...
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despit...
Pattern recognition is an area constantly enlarging its theoretical and practical horizons. Applicat...
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computat...
This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly ...
In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first...
Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article present...
Hierarchical Temporal Memory is a brain inspired memory prediction framework modeled after the unifo...
In this article we show the existence of a formal convergence between the matrix models of biologica...
Abstract. There is increasing evidence to suggest that the neocortex of the mammalian brain does not...
The main focus of this thesis lies in a rather narrow subfield of Artificial Intelligence. As any be...
Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language prese...
Sequence learning, prediction and generation has been proposed to be the universal computation perfo...
Vector-symbolic architectures (VSAs) provide methods for computing which are highly flexible and car...