Distributed vs. local representation
In R. A. Wilson & F. C. Keil (eds.), The Mit Encyclopedia of the Cognitive Sciences. Mit Press (1999)
| Abstract | been to define various notions of distribution in terms of represented by one and the same distributed pattern (Mur- structures of correspondence between the represented items dock 1979). For example, it is standard in feedforward and the representational resources (e.g., van Gelder 1992). connectionist networks for one and the same set of synap- This approach may be misguided; the essence of this alter- tic weights to represent many associations between input native category of representation might be some other prop- and output. erty entirely. For example, Haugeland (1991) has suggested • Equipotentiality In some cases, an item is represented by | |||||||||
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Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.
Tony A. Plate (2003). Holographic Reduced Representation: Distributed Representation for Cognitive Structures. Center for the Study of Language and Information.
A. Mike Burton (2000). The Many Ways to Distribute Distributed Representations. Behavioral and Brain Sciences 23 (4):472-473.
John Hawthorne (1989). On the Compatibility of Connectionist and Classical Models. Philosophical Psychology 2 (1):5-16.
Michael W. Spratling (2004). Local Versus Distributed: A Poor Taxonomy of Neural Coding Strategies. Behavioral and Brain Sciences 27 (5):700-702.
Horace Barlow & Anthony Gardner-Medwin (2000). Localist Representation Can Improve Efficiency for Detection and Counting. Behavioral and Brain Sciences 23 (4):467-468.
Peter C. M. Molenaar & Maartje E. J. Raijmakers (2000). A Phase Transition Between Localist and Distributed Representation. Behavioral and Brain Sciences 23 (4):486-486.
Tim van Gelder (1991). What is the D in PDP? In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum.
Tim van Gelder (1990). Why Distributed Representation is Inherently Non-Symbolic. In G. Dorffner (ed.), Konnektionismus in Artificial Intelligence Und Kognitionsforschung. Berlin: Springer-Verlag.
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