In this paper I offer an explanation of how the grounding of stimuli in an initial analog world can effect the interpretability of symbolic representations of the behaviour of neural networks performing cognition. I make two assertions about the form of networks powerful enough to perform cognition, first that they be composed of non-linear elements and second that their architecture is recurrent. As nets of this type are equivalent to non-linear dynamical systems I then go on to consider how the behaviour of such systems can be represented symbolically. The crucial feature of such representations is that they must be non-deterministic, they therefore differ from deterministic symbol systems such as Searle's Chinese Room. A whole range of non-deterministic symbol systems representing a single underlying continuous processes can be produced at different levels of detail. Symbols in these representations are not indivisible, if the contents of a symbol in one level of representation are known then the subsequent behaviour of that symbol system may be interpreted in terms of a more detailed representation in which non-determinism acts at a finer scale. Knowing the contents of symbols therefore effects our ability to interpret system behaviour. Symbols only have contents in a grounded system so these multiple levels of interpretation are only possible if stimuli are grounded in a finely detailed world.
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Stevan Harnad (1995). Grounding Symbols in Sensorimotor Categories with Neural Networks. Institute of Electrical Engineers Colloquium on "Grounding Representations.

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