LEADER 05063nam 22006855 450 001 9910254617203321 005 20200702054323.0 010 $a3-319-39552-1 024 7 $a10.1007/978-3-319-39552-4 035 $a(CKB)3710000000765456 035 $a(DE-He213)978-3-319-39552-4 035 $a(MiAaPQ)EBC4602939 035 $a(PPN)19451336X 035 $a(EXLCZ)993710000000765456 100 $a20160719d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aForm Versus Function: Theory and Models for Neuronal Substrates /$fby Mihai Alexandru Petrovici 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XXVI, 374 p. 150 illus., 101 illus. in color.) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 300 $a"Doctoral Thesis accepted by the University of Heidelberg, Germany." 311 $a3-319-39551-3 320 $aIncludes bibliographical references at the end of each chapters. 327 $aPrologue -- Introduction: From Biological Experiments to Mathematical Models -- Arti?cial Brains: Simulation and Emulation of Neural Networks -- Dynamics and Statistics of Poisson-Driven LIF Neurons -- Cortical Models on Neuromorphic Hardware -- Probabilistic Inference in Neural Networks -- Epilogue. 330 $aThis thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process information, how can it create and store internal models of our world, and how can it infer conclusions from ambiguous data? The author addresses these questions with the rigorous language of mathematics and theoretical physics, an approach that requires a high degree of abstraction to transfer results of wet lab biology to formal models. The thesis starts with an in-depth description of the state-of-the-art in theoretical neuroscience, which it subsequently uses as a basis to develop several new and original ideas. Throughout the text, the author connects the form and function of neuronal networks. This is done in order to achieve functional performance of biological brains by transferring their form to synthetic electronics substrates, an approach referred to as neuromorphic computing. The obvious aspect that this transfer can never be perfect but necessarily leads to performance differences is substantiated and explored in detail. The author also introduces a novel interpretation of the firing activity of neurons. He proposes a probabilistic interpretation of this activity and shows by means of formal derivations that stochastic neurons can sample from internally stored probability distributions. This is corroborated by the author?s recent findings, which confirm that biological features like the high conductance state of networks enable this mechanism. The author goes on to show that neural sampling can be implemented on synthetic neuromorphic circuits, paving the way for future applications in machine learning and cognitive computing, for example as energy-efficient implementations of deep learning networks. The thesis offers an essential resource for newcomers to the field and an inspiration for scientists working in theoretical neuroscience and the future of computing. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 606 $aPhysics 606 $aNeural networks (Computer science)  606 $aNeurobiology 606 $aNeurosciences 606 $aComputer simulation 606 $aNumerical and Computational Physics, Simulation$3https://scigraph.springernature.com/ontologies/product-market-codes/P19021 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 606 $aNeurobiology$3https://scigraph.springernature.com/ontologies/product-market-codes/L25066 606 $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 615 0$aPhysics. 615 0$aNeural networks (Computer science) . 615 0$aNeurobiology. 615 0$aNeurosciences. 615 0$aComputer simulation. 615 14$aNumerical and Computational Physics, Simulation. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aNeurobiology. 615 24$aNeurosciences. 615 24$aSimulation and Modeling. 676 $a612.8 700 $aPetrovici$b Mihai Alexandru$4aut$4http://id.loc.gov/vocabulary/relators/aut$0805094 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254617203321 996 $aForm Versus Function: Theory and Models for Neuronal Substrates$91807570 997 $aUNINA