We show that by introducing an appropriate schema concept and exploiting the higher-level features of a resonance function in a neural network it is possible to define a form of nonmonotonic inference relation between the input and the output of the network. This inference relation satisfies some of the most fundamental postulates for nonmonotonic logics. The construction presented in the paper is an example of how symbolic features can emerge from the subsymbolic level of a neural network.

Abstract: It is often claimed that the symbolic approach to information processing is incompatible with connectionism and other associationist modes of representing information. I propose to throw new light on this debate by presenting two examples of how logic can be seen as emerging from an underlying information dynamics. The first example shows how intuitionistic logic results very naturally from an abstract analysis of the dynamics of information. The second example establishes that the activities of a large class of neural networks may be interpreted, on the symbolic level, as nonmonotonic inferences. On the basis of these examples I argue that symbolic and non-symbolic approaches to information can be described in terms of different perspectives on the same phenomenon. Thus, I find that Fodor and Pylyshyn’s claim that connectionist systems cannot be systematic and compositional is based on a misleading interpretation of representations in such systems.