Statistical field theory for neural networks. (2020)

• Record Type: Book
• Further Information: Note: Moritz Helias, David Dahmen.
• Other Names: Helias, Moritz; Dahmen, David
• Contents: Introduction -- Probabilities, moments, cumulants -- Gaussian distribution and Wicks theorem -- Perturbation expansion -- Linked cluster theorem -- Functional preliminaries -- Functional formulation of stochastic differential equations -- Ornstein-Uhlenbeck process: The free Gaussian theory -- Perturbation theory for stochastic differential equations -- Dynamic mean-field theory for random networks -- Vertex generating function -- Application: TAP approximation -- Expansion of cumulants into tree diagrams of vertex functions -- Loopwise expansion of the effective action -- Tree level -- Loopwise expansion in the MSRDJ formalism -- Nomenclature.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 519.2/2; Stochastic differential equations; Neural networks (Computer science) -- Mathematics; Neurosciences; Machine learning; Mathematical modelling; Maths for computer scientists; Statistical physics; Medical -- Neuroscience; Computers -- Intelligence (AI) & Semantics; Mathematics -- Applied; Computers -- Mathematical & Statistical Software; Science -- Mathematical Physics; Neural networks (Computer science) -- Mathematics; Stochastic differential equations; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030464448
• Related ISBNs: 303046444X; 3030464431; 9783030464431
• Notes: Note: Includes bibliographical references.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.