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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.