Logic Negation with Spiking Neural P Systems
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Logic Negation with Spiking Neural P Systems Daniel Rodríguez-Chavarría1 · Miguel A. Gutiérrez-Naranjo1 · Joaquín Borrego-Díaz1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords P systems · Logic negation · Membrane computing
1 Introduction Neural networks are nowadays one of the most promising tools in computer sciences. They have been successfully applied to many real-world domains and the number of application fields is continuously increasing [15]. Beyond this doubtless success, one of the main drawbacks of such systems is that they work as black-boxes, i.e., the learned knowledge through the training process is not human-readable. Learning process in neural networks consists basically of optimizing parameters (usually a huge amount of them) guided by some type of gradient-based method and the resulting model is usually far from having semantic sense for a human researcher. In fact, the problem of explainability is becoming a new research frontier in artificial intelligence systems, even beyond machine learning [1,14]. Due to this lack of readability, new studies about the integration of neural network models (the so-called
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Joaquín Borrego-Díaz [email protected] Daniel Rodríguez-Chavarría [email protected] Miguel A. Gutiérrez-Naranjo [email protected]
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Department of Computer Science and Artificial Intelligence, University of Seville, Seville, Spain
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D. Rodríguez-Chavarría et al.
connectionist systems) and logic-based systems [4,6,17,27,31,35] can shed a new light on the future development of both research areas.1 In this context, the computational framework known as spiking neural P systems [20,21] (SN P systems, for short) provides a formal framework for the integration of both disciplines: on the one hand, they use spikes (electrical impulses) as discrete units of information as in logic-based methods and, on the other hand, their models consist of graphs where the information flows among nodes as in standard neural network architectures. SN P systems belong to the third generation of neural network models [26], the so-called integrate-and-fire spiking neuron models [13]. The integration of logic and neural networks via spikes takes advantage from an important biological fact: all the spikes inside a biological brain look alike. By using this feature, a computational binary code can be
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