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Form Versus Function: Theory and Models for Neuronal Substrates - Petrovici, Mihai Alexandru
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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 … Mehr…

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Mihai Alexandru Petrovici:

Form Versus Function: Theory and Models for Neuronal Substrates - neues Buch

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… Mehr…

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Form Versus Function: Theory and Models for Neuronal Substrates
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Form Versus Function: Theory and Models for Neuronal Substrates - neues Buch

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… Mehr…

Nr. 978-3-319-39551-7. Versandkosten:Worldwide free shipping, , DE. (EUR 0.00)
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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 - Petrovici, Mihai Alexandru
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Petrovici, Mihai Alexandru:
Form Versus Function: Theory and Models for Neuronal Substrates | Mihai Alexandru Petrovici | Buch | Springer Theses | HC runder Rücken kaschiert | XXVI | Englisch | 2016 | EAN 9783319395517 - gebunden oder broschiert

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… Mehr…

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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 - Petrovici, Mihai Alexandru
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Petrovici, Mihai Alexandru:
Form Versus Function: Theory and Models for Neuronal Substrates / Mihai Alexandru Petrovici / Buch / Springer Theses / HC runder Rücken kaschiert / XXVI / Englisch / 2016 / EAN 9783319395517 - gebunden oder broschiert

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… Mehr…

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Bibliographische Daten des bestpassenden Buches

Details zum Buch

Detailangaben zum Buch - Form Versus Function: Theory and Models for Neuronal Substrates (Springer Theses)


EAN (ISBN-13): 9783319395517
ISBN (ISBN-10): 3319395513
Gebundene Ausgabe
Erscheinungsjahr: 2016
Herausgeber: Springer

Buch in der Datenbank seit 2016-06-27T16:27:37+02:00 (Berlin)
Detailseite zuletzt geändert am 2024-01-16T10:29:19+01:00 (Berlin)
ISBN/EAN: 9783319395517

ISBN - alternative Schreibweisen:
3-319-39551-3, 978-3-319-39551-7
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: petrov
Titel des Buches: form versus function theory and models for neuronal substrates, springer theses


Daten vom Verlag:

Autor/in: Mihai Alexandru Petrovici
Titel: Springer Theses; Form Versus Function: Theory and Models for Neuronal Substrates
Verlag: Springer; Springer International Publishing
374 Seiten
Erscheinungsjahr: 2016-07-27
Cham; CH
Gedruckt / Hergestellt in Niederlande.
Gewicht: 7,214 kg
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XXVI, 374 p. 150 illus., 101 illus. in color.

BB; Numerical and Computational Physics, Simulation; Hardcover, Softcover / Physik, Astronomie/Allgemeines, Lexika; 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; Mathematical Models of Cognitive Processes and Neural Networks; Neurobiology; Neurosciences; Simulation and Modeling; 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.

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