  Nowadays, the success of neural networks as reasoning systems is doubtless.
Nonetheless, one of the drawbacks of such reasoning systems is that they work
as black-boxes and the acquired knowledge is not human readable. In this paper,
we present a new step in order to close the gap between connectionist and logic
based reasoning systems. We show that two of the most used inference rules for
obtaining negative information in rule based reasoning systems, the so-called
Closed World Assumption and Negation as Finite Failure can be characterized by
means of spiking neural P systems, a formal model of the third generation of
neural networks born in the framework of membrane computing.
