2016, ISBN: 9783319395517
[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This thesis addresses one of the most fundamental challenges for modern science: how can the brain … Plus…
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2016, ISBN: 3319395513
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… Plus…
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ISBN: 9783319395517
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… Plus…
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Form Versus Function: Theory and Models for Neuronal Substrates | Mihai Alexandru Petrovici | Buch | Springer Theses | HC runder Rücken kaschiert | XXVI | Englisch | 2016 | EAN 9783319395517 - edition reliée, livre de poche
2016, ISBN: 9783319395517
[ED: Gebunden], [PU: Springer International Publishing], This thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process… Plus…
booklooker.de |
Form Versus Function: Theory and Models for Neuronal Substrates / Mihai Alexandru Petrovici / Buch / Springer Theses / HC runder Rücken kaschiert / XXVI / Englisch / 2016 / EAN 9783319395517 - edition reliée, livre de poche
2016, ISBN: 9783319395517
[ED: Gebunden], [PU: Springer International Publishing], This thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process… Plus…
booklooker.de |
2016, ISBN: 9783319395517
[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This thesis addresses one of the most fundamental challenges for modern science: how can the brain … Plus…
Mihai Alexandru Petrovici:
Form Versus Function: Theory and Models for Neuronal Substrates - nouveau livre2016, ISBN: 3319395513
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… Plus…
ISBN: 9783319395517
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… Plus…
Form Versus Function: Theory and Models for Neuronal Substrates | Mihai Alexandru Petrovici | Buch | Springer Theses | HC runder Rücken kaschiert | XXVI | Englisch | 2016 | EAN 9783319395517 - edition reliée, livre de poche
2016, ISBN: 9783319395517
[ED: Gebunden], [PU: Springer International Publishing], This thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process… Plus…
Form Versus Function: Theory and Models for Neuronal Substrates / Mihai Alexandru Petrovici / Buch / Springer Theses / HC runder Rücken kaschiert / XXVI / Englisch / 2016 / EAN 9783319395517 - edition reliée, livre de poche
2016, ISBN: 9783319395517
[ED: Gebunden], [PU: Springer International Publishing], This thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process… Plus…
Données bibliographiques du meilleur livre correspondant
Informations détaillées sur le livre - Form Versus Function: Theory and Models for Neuronal Substrates (Springer Theses)
EAN (ISBN-13): 9783319395517
ISBN (ISBN-10): 3319395513
Version reliée
Date de parution: 2016
Editeur: Springer
Livre dans la base de données depuis 2016-06-27T16:27:37+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-01-16T10:29:19+01:00 (Zurich)
ISBN/EAN: 9783319395517
ISBN - Autres types d'écriture:
3-319-39551-3, 978-3-319-39551-7
Autres types d'écriture et termes associés:
Auteur du livre: petrov
Titre du livre: form versus function theory and models for neuronal substrates, springer theses
Données de l'éditeur
Auteur: Mihai Alexandru Petrovici
Titre: Springer Theses; Form Versus Function: Theory and Models for Neuronal Substrates
Editeur: Springer; Springer International Publishing
374 Pages
Date de parution: 2016-07-27
Cham; CH
Imprimé / Fabriqué en
Langue: Anglais
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XXVI, 374 p. 150 illus., 101 illus. in color.
BB; Hardcover, Softcover / Physik, Astronomie/Theoretische Physik; Mathematische Physik; Verstehen; Theoretical Neuroscience; Computational Neuroscience; Neuromorphic Hardware; Neural Network Theory; Neuronal Dynamics; Abstract Spiking Neuron Models; Spike and Rate Codes; Neural Sampling; Bayesian Inference; Deep Learning Architectures; Theoretical, Mathematical and Computational Physics; Mathematical Models of Cognitive Processes and Neural Networks; Neuroscience; Computer Modelling; Mathematische Modellierung; Neurowissenschaften; Computermodellierung und -simulation; EA; BC
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.Nominated as an outstanding PhD thesis by Heidelberg University, Germany Provides an excellent state-of-the-art overview of theoretical neuroscience An inspiration for newcomers to engage in this fascinating and future-oriented field of research Includes supplementary material: sn.pub/extras
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