On the Nonlearnability of a Single Spiking Neuron

DOI10.1162/089976605774320601zbMath1087.68083OpenAlexW2138927477WikidataQ51962630 ScholiaQ51962630MaRDI QIDQ3370746

Jiří Sgall, Jiří Šíma

Publication date: 8 February 2006

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/089976605774320601

zbMATH Keywords

neural networksBoolean function

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• The densest hemisphere problem
• Complexity theoretic hardness results for query learning
• On computing Boolean functions by a spiking neuron
• On the complexity of learning for spiking neurons with temporal coding.
• Robust trainability of single neurons
• On the geometric separability of Boolean functions
• Learnability and the Vapnik-Chervonenkis dimension
• A theory of the learnable
• Computational limitations on learning from examples
• Training a Single Sigmoidal Neuron Is Hard

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