Displaying similar documents to “Preface”

Generating Networks of Splicing Processors

Jürgen Dassow, Florin Manea, Bianca Truthe (2012)

RAIRO - Theoretical Informatics and Applications

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In this paper, we introduce generating networks of splicing processors (GNSP for short), a formal languages generating model related to networks of evolutionary processors and to accepting networks of splicing processors. We show that all recursively enumerable languages can be generated by GNSPs with only nine processors. We also show, by direct simulation, that two other variants of this computing model, where the communication between...

Generating Networks of Splicing Processors

Jürgen Dassow, Florin Manea, Bianca Truthe (2012)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

Similarity:

In this paper, we introduce generating networks of splicing processors (GNSP for short), a formal languages generating model related to networks of evolutionary processors and to accepting networks of splicing processors. We show that all recursively enumerable languages can be generated by GNSPs with only nine processors. We also show, by direct simulation, that two other variants of this computing model, where the communication between processors is conducted in different ways, have...

Comparison of supervised learning methods for spike time coding in spiking neural networks

Andrzej Kasiński, Filip Ponulak (2006)

International Journal of Applied Mathematics and Computer Science

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In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods?...

A chunking mechanism in a neural system for the parallel processing of propositional production rules.

Ernesto Burattini, A. Pasconcino, Guglielmo Tamburrini (1995)

Mathware and Soft Computing

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The problem of extracting more compact rules from a rule-based knowledge base is approached by means of a chunking mechanism implemented via a neural system. Taking advantage of the parallel processing potentialities of neural systems, the computational problem normally arising when introducing chuncking processes is overcome. Also the memory saturation effect is coped with using some sort of forgetting mechanism which allows the system to eliminate previously stored, but less often...