The Role of Attention in the Context of Associative Memory

Wichert, Andreas

doi:10.1007/s12559-010-9064-1

Cited by 11 publications

(13 citation statements)

References 17 publications

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“…In applications where the number of simultaneously active superpositions generated by the sensory system exceeds this limit, attention and binding mechanisms are needed to select and bind objects of interest before they enter working memory. Attention is beyond the scope of this work but is actively studied in various fields, mostly in the perspective of biological and machine vision systems (Treisman, 1998;Reynolds & Desimone, 1999;Itti & Koch, 2001;Wichert, 2011;Allen, Hitch, Mate, & Baddeley, 2012).…”

Section: Working Memorymentioning

confidence: 99%

Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2015

Biologically Inspired Cognitive Architectures

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The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems automatically interoperable using handcrafted ''semantic'' descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration. ª 2015 Published by Elsevier B.V.

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Section: Working Memorymentioning

confidence: 99%

Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2015

Biologically Inspired Cognitive Architectures

View full text Add to dashboard Cite

show abstract

“…In applications where the number of simultaneously active superpositions gener-ated by the sensory system exceeds this limit, attention and binding mechanisms are needed to select and bind objects of interest before they enter working memory. Attention is beyond the scope of this work but is actively studied in various fields, mostly in the perspective of biological and machine vision systems (Treisman, 1998;Reynolds and Desimone, 1999;Itti and Koch, 2001;Wichert, 2011;Allen et al, 2012).…”

Section: Working Memorymentioning

confidence: 99%

Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2014

Biologically Inspired Cognitive Architectures

View full text Add to dashboard Cite

The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration.

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“…However if the precondition (set of objects) is stored in an associative memory the task of proto logic is non trivial Wichert [2011]. In an associative memory direct access to the stored information is not present.…”

Section: Proto Logicmentioning

confidence: 99%

Proto logic and neural subsymbolic reasoning

Wichert

2012

Journal of Logic and Computation

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The sub-symbolic representation of the world often corresponds to a pattern that mirrors the world as described by the biological sense organs. Sparse binary vectors can describe sub-symbolic representations, which can be efficiently stored in associative memories. According to the production system theory, a geometrically based problem-solving model can be defined as a production system operating on sub-symbols. Our goal is to form a sequence of associations, which lead to a desired state represented by sub-symbols, from an initial state represented by sub-symbols. A simple and universal heuristic function can be defined, which takes into account the relationship between the vector and the corresponding similarity of the represented object or state in the real world. The manipulation of the sub-symbols is described by a simple proto logic, which verifies if a subset of sub-symbols is present in a set of sub-symbols.

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The Role of Attention in the Context of Associative Memory

Cited by 11 publications

References 17 publications

Vector space architecture for emergent interoperability of systems by learning from demonstration

Vector space architecture for emergent interoperability of systems by learning from demonstration

Vector space architecture for emergent interoperability of systems by learning from demonstration

Proto logic and neural subsymbolic reasoning

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