Logic Negation with Spiking Neural P Systems

TL;DR

This paper demonstrates how spiking neural P systems can model logical negation and reasoning rules, bridging the gap between neural networks and logic-based reasoning systems.

Contribution

It introduces a novel approach to represent logical negation and inference rules using spiking neural P systems, linking neural models with logical reasoning.

Findings

Spiking neural P systems can simulate the Closed World Assumption.

They can also model Negation as Finite Failure.

This approach enhances interpretability of neural reasoning systems.

Abstract

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.