Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché

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

Format: Broché

Note : 0

Soyez le premier à donner un avis

Vous en avez un à vendre ?

Vendez-le-vôtre

Filtrer par :

Neuf (1)

Occasion (1)

Reconditionné

132,49 €

Occasion · Comme Neuf

Ou 33,12 € /mois

Livraison : 25,00 €
Livré entre le 6 et le 15 mai

Voir les modes de livraison

PRO Vendeur favori

4,6/5 sur + de 1 000 ventes

Service client à l'écoute et une politique de retour sans tracas - Livraison des USA en 3 a 4 semaines (2 mois si circonstances exceptionnelles) - La plupart de nos titres sont en anglais, sauf indication contraire. N'hésitez pas à nous envoyer un e-... Voir plus

Nos autres offres

250,74 €

Produit Neuf

Ou 62,69 € /mois

En savoir plus
- Livraison : 25,00 €
- Livré entre le 13 et le 18 mai
Voir les modes de livraison
Kelindo

PRO

4,8/5 sur + de 1 000 ventes

Contacter le vendeur
Afficher la description de l'annonce
Apres acceptation de la commande, le delai moyen d'expedition depuis le Japon est de 48 heures. Le delai moyen de livraison est de 3 a 4 semaines. En cas de circonstances exceptionnelles, les delais peuvent s'etendre jusqu'à 2 mois.

Vous avez choisi le retrait chez le vendeur à

Payez directement sur Rakuten (CB, PayPal, 4xCB...)
Récupérez le produit directement chez le vendeur
Rakuten vous rembourse en cas de problème

Terminer ma commande

Gratuit et sans engagement

Félicitations !

Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

TROUVER UN MAGASIN

Adresse

Retour

Horaires

Note :

Avis sur Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché - Livre Loisirs

Note : 0 0 avis sur Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché - Livre Loisirs

Donnez votre avis

Donnez votre avis sur ce produit

Votre note * : 1/5 2/5 3/5 4/5 5/5 Veuillez donner une note au produit

Le titre de votre avis* : Saisissez un titre

Quelques conseils :

Donnez le ton de votre avis, résumez-le en quelques mots qui apparaîtront comme le titre de votre avis.

Votre avis* : (40 caractères minimum) Saisissez un avis

Quelques conseils :

N'hésitez pas à mettre en avant les caractéristiques de ce produit, n'oubliez pas de mentionner ses avantages et/ou ses défauts éventuels. Décrivez précisément, argumentez, soyez convaincant !

Annuler

Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.

Produits similaires

Présentation Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché
- Livre Loisirs

Livre Loisirs - Petrovici, Mihai Alexandru - 30/04/2018 - Broché - Langue : Anglais

Auteur(s) : Petrovici, Mihai Alexandru

Editeur : Springer International Publishing Ag

Langue : Anglais

Parution : 30/04/2018

Format : Moyen, de 350g à 1kg

Nombre de pages : 400

Expédition : 604

Dimensions : 23.5 x 15.5 x 2.2

ISBN : 3319819135

Résumé :

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 transfercan 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.

Biographie:
Mihai Petrovici started studying Physics at the University of Heidelberg in 2001. During his early undergraduate days, he worked on particle tracking for the ALICE experiment at CERN. For his diploma thesis, he moved to solid state physics, where he studied glasses at low temperatures. He began his PhD in 2008 in the Electronic Vision(s) group of Karlheinz Meier and Johannes Schemmel, where he worked at the interface of theoretical neuroscience and neuromorphic computing, earning his doctorate with summa cum laude in 2015. During this time, he established a theoretical department within the Vision(s) group, which he is currently leading....

Sommaire:

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.

...

Détails de conformité du produit

Consulter les détails de conformité de ce produit (

Personne responsable dans l'UE

)

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

Avis sur Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché - Livre Loisirs

Présentation Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché - Livre Loisirs

Présentation Form Versus Function: Theory And Models For Neuronal Substrates de Petrovici, Mihai Alexandru Format Broché
- Livre Loisirs