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Titlebook: 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997; Connectionist Repres John A. Bullinaria,David W. Glasspool,George

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發(fā)表于 2025-3-21 16:07:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997
期刊簡(jiǎn)稱Connectionist Repres
影響因子2023John A. Bullinaria,David W. Glasspool,George Hough
視頻videohttp://file.papertrans.cn/101/100913/100913.mp4
學(xué)科分類Perspectives in Neural Computing
圖書(shū)封面Titlebook: 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997; Connectionist Repres John A. Bullinaria,David W. Glasspool,George
影響因子This volume collects together refereed versions of twenty-five papers presented at the 4th Neural Computation and Psychology Workshop, held at University College London in April 1997. The "NCPW" workshop series is now well established as a lively forum which brings together researchers from such diverse disciplines as artificial intelligence, mathematics, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their work on connectionist modelling in psychology. The general theme of this fourth workshop in the series was "Connectionist Repre- sentations", a topic which not only attracted participants from all these fields, but from allover the world as well. From the point of view of the conference organisers focusing on representational issues had the advantage that it immediately involved researchers from all branches of neural computation. Being so central both to psychology and to connectionist modelling, it is one area about which everyone in the field has their own strong views, and the diversity and quality of the presentations and, just as importantly, the discussion which followed them, certainly attested to this.
Pindex Conference proceedings 1998
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書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997影響因子(影響力)




書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997影響因子(影響力)學(xué)科排名




書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997被引頻次




書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997被引頻次學(xué)科排名




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書(shū)目名稱4th Neural Computation and Psychology Workshop, London, 9–11 April 1997年度引用學(xué)科排名




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https://doi.org/10.1007/978-3-031-51521-7 a clear distinction between the two types of model and address some of the reasons for this popular bias. I conclude that, contrary to conventional wisdom, a localist approach shows more promise than a fully-distributed approach with regard to the development of both psychological-level and brain-l
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Lecture Notes in Civil Engineeringoperties of multi layer perceptrons (MLPs). In particular, the highly regarded use of distributed representations in MLPs is established in RBF networks. RBF networks have been used to learn a categorization task, and the way in which the networks have learned the task is interpreted in terms of loc
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Mohammad Nadimi,Jitendra Paliwala novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learning method.
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Zafer Serin,U?ur Yüzge?,Cihan Karakuzurent ways of quantifying the degree of topography of a mapping have been proposed. In order to investigate the consequences of the varying assumptions that these different approaches embody, we have optimized the mapping with respect to a number of different measures for a very simple problem — the
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Family Caregiver Stress in Critical Care“selection network” to achieve a mapping from their location on the retina to a translation-invariant “focus of attention”. We investigated spatially selective lesions affecting either the mapping from one side of the retina into the selection network, or the mapping from the selection network to on
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