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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

31.07.2020

Logic Negation with Spiking Neural P Systems

verfasst von: Daniel Rodríguez-Chavarría, Miguel A. Gutiérrez-Naranjo, Joaquín Borrego-Díaz

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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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.
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Fußnoten
1
A recent survey in neural-symbolic learning and reasoning can be found in [5].
 
2
A detailed description of the controversy generated around the use of the negation in deductive databases is out of the scope of this paper. More information is available in [9, 22].
 
3
In the literature, many different SN P systems models have been presented. In this paper, a simple model is considered.
 
4
Let us remark that, according to the definition, \(I(\rightarrow A) = 1\) if and only if \(I(A) = 1\).
 
5
A detailed description can be found in [25].
 
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Metadaten
Titel
Logic Negation with Spiking Neural P Systems
verfasst von
Daniel Rodríguez-Chavarría
Miguel A. Gutiérrez-Naranjo
Joaquín Borrego-Díaz
Publikationsdatum
31.07.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10324-6

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