Statistical Field Theory for Neural Networks
Description
Statistical Field Theory for Neural Networks By Helias, Moritz Published by Springer Publication Date: 2020-08-21 Subject: Computers Artificial Intelligence General, Computers Mathematical & Statistical Software, Computers Artificial Intelligence - General, Computers Mathematical & Statistical Software, Mathematics Applied, Medical Neuroscience, Mathematics Applied, Medical Neuroscience, Science Physics Mathematical & Computational, Science Physics - Mathematical & Computational, Machine Learning, Mathematical & Statistical Software, Mathematical Modelling, Maths For Computer Scientists, Neurosciences, Statistical Physics, Mathematical Physics, Probability And Statistics, Statistical Physics, Neurosciences, Machine Learning, Mathematical Modelling, Maths For Computer Scientists, Mathematical & Statistical Software, Science Physics Mathematical & Computational, Science, Physics, Mathematical & Computational, Medical Neuroscience, Medical, Neuroscience, Computers Artificial Intelligence General, Computers, Artificial Intelligence, General, Mathematics Applied, Mathematics, Applied, Computers Mathematical & Statistical Software, Mathematical & Statistical Software, Mathematical Physics, Probability And Statistics, Anf: Science Subject Keywords: Statistical physics; Neuronal networks; Dynamic mean-field theory; Diagrammatic techniques; Chaotic network dynamics; Correlated neuronal activity Genre: Statistical Physics, Neurosciences, Machine Learning, Mathematical Modelling, Maths For Computer Scientists, Mathematical & Statistical Software, Science, Physics, Mathematical & Computational, Medical, Neuroscience, Computers, Artificial Intelligence, General, Mathematics, Applied, Computers, Mathematical & Statistical Software, Mathematical Physics, Neurosciences, Machine Learning, Mathematical Modelling, Maths For Computer Scientists, Probability And Statistics Target Audience: Professional and scholarly
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