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Form versus function [electronic resource] : theory and models for neuronal substrates

Form versus function [electronic resource] : theory and models for neuronal substrates

자료유형
E-Book(소장)
서명 / 저자사항
Form versus function [electronic resource] : theory and models for neuronal substrates / Mihai Alexandru Petrovici.
발행사항
Cham : Springer International Publishing : Imprint: Springer, 2016.
형태사항
1 online resource (xxvi, 374 p.) : ill. (some col.).
총서사항
Springer theses, recognizing outstanding Ph.D. research,2190-5053
ISBN
9783319395524
요약
This 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.
일반주기
Title from e-Book title page. "Doctoral thesis accepted by the University of Heidelberg, Germany."
내용주기
Prologue -- Introduction: From Biological Experiments to Mathematical Models -- Artificial 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.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.
일반주제명
Physics. Computational neuroscience. Neural circuitry.
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100 1 ▼a Petrovici, Mihai Alexandru.
245 1 0 ▼a Form versus function ▼h [electronic resource] : ▼b theory and models for neuronal substrates / ▼c Mihai Alexandru Petrovici.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2016.
300 ▼a 1 online resource (xxvi, 374 p.) : ▼b ill. (some col.).
490 1 ▼a Springer theses, recognizing outstanding Ph.D. research, ▼x 2190-5053
500 ▼a Title from e-Book title page.
500 ▼a "Doctoral thesis accepted by the University of Heidelberg, Germany."
504 ▼a Includes bibliographical references.
505 0 ▼a Prologue -- Introduction: From Biological Experiments to Mathematical Models -- Artificial 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.
520 ▼a This 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Physics.
650 0 ▼a Computational neuroscience.
650 0 ▼a Neural circuitry.
830 0 ▼a Springer theses, recognizing outstanding Ph.D. research.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-39552-4
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 612.8/233 등록번호 E14022615 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

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