The status of the subsymbolic hypothesis remains an issue for debate and further research. Certainly it is unclear how symbolic approaches could match certain characteristics of neural network approaches, such as their ability to cope with novel instances and their graceful degradation in the face of errors or omissions. On the other hand, some neural network architectures for storing knowledge and performing inference have been shown (or designed) to be closely related to “soft logics” such as fuzzy logic (e.g., Kasabov 1996; Kecman 2001) or “weight-annotated Poole systems” (Blutner 2004), suggesting the possibility that neural network models of cognition may ultimately be characterizable as implementations of such soft logics. Researchers more concerned with practical advances than biologically plausible modeling have also explored the possibility of hybridizing the symbolic and subsymbolic approaches, in order to gain the advantages of both (e.g., Sun 2001). A quite formal example of this, drawing on ideas by Dov Gabbay, is d'Avila Garcez (2004).