Open AccessA comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms
Author(s) -
Alexander Sboev,
Danila Vlasov,
Alexey Serenko,
Roman Rybka,
Ivan Moloshnikov
Publication year - 2016
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/681/1/012013
Subject(s) - spike timing dependent plasticity , spike (software development) , computer science , pairing , signal (programming language) , spiking neural network , artificial intelligence , process (computing) , scheme (mathematics) , plasticity , biological system , pattern recognition (psychology) , machine learning , artificial neural network , mathematics , synaptic plasticity , biology , physics , mathematical analysis , biochemistry , receptor , superconductivity , software engineering , thermodynamics , quantum mechanics , programming language , operating system