03068nam a22004215i 4500001001800000003000900018008004100027020001800068024003500086035002000121041000800141082001400149100002800163245009400191250002200285264007000307300008200377336002600459337002600485338003600511347002400547490003600571500001300607505065700620520092001277650002202197650002902219650004202248650001902290650002502309700002702334856005602361915001202417932005402429932004902483596000602532949010802538978-3-030-46444-8Springer200820s2020 gw | s |||| 0|eng d a97830304644487 a10.1007/978-3-030-46444-82doi a(Sirsi) a815190 aeng04a530.12231 aHelias, Moritz.eauthor10aStatistical Field Theory for Neural Networksh[E-Book] /cby Moritz Helias, David Dahmen. a1st edition 2020. 1aCham :bSpringer,c2020e(Springer LINK)fSpringerPhysics20200825 aXVII, 203 pages 127 illustrations, 5 illustrations in color (online resource) atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda aLecture Notes in Physics ;v970 aenglisch0 aIntroduction -- Probabilities, moments, cumulants -- Gaussian distribution and Wick's 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. aThis book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra. 0aMachine learning. 0aMathematical statistics. 0aNeural networks (Computer science) . 0aNeurosciences. 0aStatistical physics.1 aDahmen, David,eauthor40uhttps://doi.org/10.1007/978-3-030-46444-8zVolltext azzwFZJ3 aPhysics and Astronomy (R0) (SpringerNature-43715) aPhysics and Astronomy (SpringerNature-11651) a1 aXX(815190.1)wAUTOc1i815190-1001lELECTRONICmZBrNsYtE-BOOKu25/8/2020xZB-PzUNKNOWN0NEL1ONLINE