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Titlebook: Connectionist Models in Cognitive Neuroscience; The 5th Neural Compu Dietmar Heinke,Glynn W. Humphreys,Andrew Olson Conference proceedings

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書目名稱Connectionist Models in Cognitive Neuroscience
副標(biāo)題The 5th Neural Compu
編輯Dietmar Heinke,Glynn W. Humphreys,Andrew Olson
視頻videohttp://file.papertrans.cn/236/235618/235618.mp4
概述Will provide a state of the art survey of the interplay between cognitive neuroscience and psychology Will provide background reading for final year option courses and masters courses in cognitive sci
叢書名稱Perspectives in Neural Computing
圖書封面Titlebook: Connectionist Models in Cognitive Neuroscience; The 5th Neural Compu Dietmar Heinke,Glynn W. Humphreys,Andrew Olson Conference proceedings
描述1. Introdudion This volume collects together the refereed versions of 25 papers presented at the 5th Neural Computation and Psychology Workshop (NCPW5), held at the University of Birmingham from the 8th until the lOth of September 1998. The NCPW is a well-established, lively forum, which brings together researchers from a range of disciplines (artificial intelligence, mathematics, cognitive science, computer science, neurobiology, philosophy and psychology), all of whom are interested in the application of neurally-inspired (connectionist) models to topics in psychology. The theme of the 5th workshop in the series was Connectionist models in cognitive neuroscience‘, and the workshop aimed to bring together papers focused on the inter-relations between functional (psychological) accounts of cognition and neural accounts of underlying brain processes, linked by connectionist models. From the very beginnings of modern psychology, with the work of William James and his contemporaries, researchers have believed it important to relate behavioural analyses to neurological underpinnings. However, with the advent of connectionist modelling, where models are at least inspired by neuronal pro
出版日期Conference proceedings 1999
關(guān)鍵詞artificial intelligence; cognition; control; cortex; emotion; intelligence; mathematics; memory; model; model
版次1
doihttps://doi.org/10.1007/978-1-4471-0813-9
isbn_softcover978-1-85233-052-1
isbn_ebook978-1-4471-0813-9Series ISSN 1431-6854
issn_series 1431-6854
copyrightSpringer-Verlag London Limited 1999
The information of publication is updating

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Competitive Queuing and Spelling: Modelling Acquired Dysgraphiality of a competitive queuing model of spelling to reproduce a novel pattern of acquired dysgraphia which is characterised by reduced activation of lexical-orthographic representations. The model is able to account for some aspects of performance reasonably well. For instance, better spelling of let
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Neuropsychologically plausible sequence generation in a multi-layer network model of spellingre. The approach is applied to a model of output processes in spelling, and we show that it provides an explanation for so-called ‘Graphemic Buffer Disorder’. We describe a patient with an apparently novel dysgraphia affecting the start of words, and show that this can also be explained in terms of
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發(fā)表于 2025-3-22 12:45:04 | 只看該作者
Connectionist Dissociations, Confounding Factors and Modularityociations and what they really mean, especially when connectionist or neural network models are involved. In this paper I attempt to clarify matters by looking at the subject from the point of view of patterns of learning rates in neural network models.
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Short Term Memory and Selection Processes in a Frontal-Lobe Modele selection. In the model, reverberation states are sustained after stimulus offset, due to loops of recurrent excitation in neural cell assemblies and lateral inhibition is necessary to block an uncontrolled spread of activation. At high levels of inhibition the system performs response selection,
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A Connectionist Model for Frequency Effects in Recall and Recognitionion from these findings in from of a high frequency advantage of cues in recognition, an advantage for recognition of words over non-words, and a lack of frequency effects in mixed list of recall. A distributed connectionist memory model consisting of two mechanism sensitive to frequency is suggeste
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