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Connectionist Models
- OXFORD COMPANION TO CONSCIOUSNESS
, 2009
"... Connectionist models, also known as Parallel Distributed Processing (PDP) models, are a class of computational models often used to model aspects of human perception, cognition, and behaviour, the learning processes underlying such behaviour, and the storage and retrieval of information from memory. ..."
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Cited by 4 (0 self)
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Connectionist models, also known as Parallel Distributed Processing (PDP) models, are a class of computational models often used to model aspects of human perception, cognition, and behaviour, the learning processes underlying such behaviour, and the storage and retrieval of information from memory
On connectionist models
, 1987
"... The DEVS formalism supports modeling of discrete event systems in a hierarchical, modular manner based on the ob-ject-oriented world view. System modeling requires not only understanding of modeling framework but also domain kno-wledge of the system. Therefore, successful modeling may need a means t ..."
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Cited by 34 (3 self)
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The DEVS formalism supports modeling of discrete event systems in a hierarchical, modular manner based on the ob-ject-oriented world view. System modeling requires not only understanding of modeling framework but also domain kno-wledge of the system. Therefore, successful modeling may need a means
Biological constraints on connectionist modelling
- Connectionism in Perspective
, 1989
"... Many researchers interested in connectionist models accept that such models are "neurally inspired " but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how bio ..."
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Cited by 90 (11 self)
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Many researchers interested in connectionist models accept that such models are "neurally inspired " but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how
Connectionist models of development
, 2003
"... How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical pe ..."
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Cited by 47 (5 self)
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How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical
Shortlist: a connectionist model of continuous speech recognition
- Cognition
, 1994
"... Previous work has shown how a back-propagation network with recurrent connections can successfully model many aspects of human spoken word recogni-tion (Norris, 1988, 1990, 1992, 1993). However, such networks are unable to revise their decisions in the light of subsequent context. TRACE (McClelland ..."
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Cited by 324 (14 self)
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Previous work has shown how a back-propagation network with recurrent connections can successfully model many aspects of human spoken word recogni-tion (Norris, 1988, 1990, 1992, 1993). However, such networks are unable to revise their decisions in the light of subsequent context. TRACE (Mc
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
, 1995
"... Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical s ..."
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Cited by 675 (39 self)
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synapses change a little on each reinstatement, and that remote memory is based on accumulated neocortical changes. Models that learn via changes to connections help explain this organization. These models discover the structure in ensembles of items if learning of each item is gradual and interleaved
Connectionist Models of Language Processing
- COGN. STUD
, 2003
"... Traditional approaches to language processing have been based on explicit, discrete representations which are difficult to learn from a reasonable linguistic environment—hence, it has come to be accepted that much of our linguistic representations and knowledge is innate. With its focus on learning ..."
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based upon graded, malleable, distributed representations, connectionist modeling has reopened the question of what could be learned from the environment in the absence of detailed innate knowledge. This paper provides an overview of connectionist models of language processing, at both the lexical
Connectionist Models in Developmental Cognitive . . .
"... Connectionist models have made significant contributions to understanding developmental phenomena, mainly by providing novel computational accounts of behavioral emergence and change. What is the fate of such models given the increasing interest in and information about the biological bases of devel ..."
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Cited by 16 (0 self)
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Connectionist models have made significant contributions to understanding developmental phenomena, mainly by providing novel computational accounts of behavioral emergence and change. What is the fate of such models given the increasing interest in and information about the biological bases
Time in Connectionist Models
- Sequence Learning: Paradigms, Algorithms, and Applications
, 2001
"... Introduction The prototypical use of "classical" connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time. I ..."
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Introduction The prototypical use of "classical" connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time
Time in connectionist models
- Sequence Learning: Paradigms, Algorithms, and Applications
, 2001
"... The prototypical use of “classical ” connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time. In response to this, tem ..."
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Cited by 2 (0 self)
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The prototypical use of “classical ” connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time. In response to this
Results 1 - 10
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1,857