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By Gary F. Marcus

An try and combine theories approximately how the brain works, person who says that the brain is a computer-like manipulator of symbols, and one other that claims that the brain is a huge community of neurons operating jointly in parallel.

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Although multilayer perceptrons typically have relatively little innate structure, it is possible in principle to prespecify their connection weights (for an example of a system in which connection weights are in fact to some extent prespecified, see Nolfi, Elman & Parisi, 1994). , Newell, 1990). Finally, although it is true that one could argue that multilayer perceptrons are more parsimonious than symbolic models, one could equally argue that they are less parsimonious. As McCloskey (1991) Multilayer Perceptrons 31 notes, one could argue that networks with thousands of connection weights have thousands of free parameters.

Chomsky & Halle, 1968). My point is that attempts to differentiate multilayer perceptrons from symbol-manipulators cannot rest on questions such as whether there are context-independent mental representations of categories or whether mental representations are distributed. Indeed, one might argue that we ought to look elsewhere in trying to differentiate multilayer perceptrons and symbol-manipulators. For example, for Vera and Simon (1994, p. 360), multilayer “connectionist systems certainly differ in important respects from ‘classical’ [symbol-manipulating] simulations of human cognition .

Bottom panel: An input encoding scheme in which a set of nodes is devoted to the encoding of each variable. In the top panel, both the number of weights and the distance from the fulcrum are encoded locally. In the bottom panel, both the number of weights and the distance from the fulcrum are encoded in distributed fashion, using banks of nodes. 0 the 3 node in the bank of weights representing the number of weights. Hidden units and output units are not shown. localist and distributed representations.

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