Refined Kolmogorov complexity of analog, evolving and stochastic recurrent neural networks

Kolmogorov complexity measures the compressibility of real numbers. We provide a refined characterization of the hypercomputational power of analog, evolving, and stochastic neural networks based on the Kolmogorov complexity of their real weights, evolving weights, and real probabilities, respective...

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Bibliographic Details
Published in:	Information sciences Vol. 711; p. 122104
Main Authors:	Cabessa, Jérémie, Strozecki, Yann
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.09.2025
Subjects:	Analog computation Computability theory Computational power Echo state networks Kolmogorov complexity Quantization Recurrent neural networks Stochastic computation Recurrent neural networks Computability theory Computational power Echo state networks Kolmogorov complexity Quantization Analog computation Stochastic computation
ISSN:	0020-0255
Online Access:	Get full text
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