Document (#43079)

Editor

Ramacher, U.
Malsburg, C. von der

Title

Zur Konstruktion künstlicher Gehirne

Imprint

Berlin : Springer

Year

2009

Pages

VIII, 391 S

Isbn

978-3-642-00187-1

Abstract

In diesem Buch wird eine erste Generation von künstlichen Hirnen für das Sehen vorgestellt. Auf der ausschließlichen Grundlage von Neuron- und Synapsenmodellen wird ein Objekterkennungssystem konstruiert, welches eine Merkmalspyramide mit 8 Orientierungen und 5 Auflösungsskalen für 1000 Objekte sowie die Netze für die Bindung von Merkmalen zu Objekten umfasst. Dieses Sehsystem kann unabhängig von der Beleuchtung, dem Gesichtausdruck, der Entfernung und einer Drehung, welche die Objektkomponenten sichtbar lässt, Objekte erkennen. Seine Realisierung erfordert 59 Chips - davon sind 4 verschieden - welche mittels 3D Technologie zu einem Quader von 8mm x 8mm x 1mm aufgeschichtet sind. Die Leistungsfähigkeit des neuronal-synaptischen Netzwerkes beruht auf der Einführung von schnell veränderlichen dynamischen Synapsen. Anders als Netze mit konstanten Synapsen können solche mit dynamischen Synapsen allgemeine Aufgaben der Mustererkennung übernehmen. Die raum-zeitliche Korrelationsstruktur von Mustern wird durch eine einzige synaptische Differentialgleichung in universeller Weise erfasst. Die Korrelation kann in Erscheinung treten als synchrone Pulstätigkeit einer Neurongruppe, wodurch das Vorliegen eines Merkmals in robuster Weise angezeigt wird, oder als Bindung von Merkmalen zu Objekten.
Auch wenn die Autoren der Überzeugung sind, dass noch viele Generationen folgen müssen, um die Leistungsfähigkeit des menschlichen Gehirns zu erreichen, sehen sie doch ein neues Rechen-Zeitalter aufziehen. Es gab Zeiten, da wurden Computer mit ihrer Präzision, Zuverlässigkeit und rasanten Geschwindigkeit der feuchten Materie unseres Gehirns als so weit überlegen angesehen wie das Düsenflugzeug dem Spatzen. Dass diese Zeiten vorbei sind, ist gewiss, denn durch formale Logik inspirierte, algorithmisch gesteuerte und mit digitaler Elektronik realisierte Systeme, die heutigen Computer, stoßen an ihre Komplexitätsgrenzen. Andererseits eröffnen die hier vorgestellten Ergebnisse den Weg zu einer Alternative. Ein Paradigmenwechsel liegt in der Luft: vom fremdorganisierten zum selbstorganisierten Computer.

Content

1. Problematik der Modellierung künstlicher Gehirne - 2. Informationsverarbeitung in Netzen mit konstanten Synapsen - 3. Allgemeine Theorie der Netze mit dynamischen Synapsen - 4. Makrodynamik der Netze mit konstanten Synapsen - 5. Informationsverarbeitung mit dynamischen Synapsen - 6. Netze für die Merkmalsdetektion - 7. Netze für die Merkmalserkennung - 8. Netze für die robuste Kopfdetektion - 9. Ausblick - 10. Vor üb er legungen zur mikroelektronischen Realisierung - 11. Elementare Schaltungen für Neuronen, Synapsen und Photosensoren - 12. Simulation mikroelektronischer neuronaler Schaltungen und Systeme - 13. Architektur und Chip-Entwurf des Merkmalserkenners - 14. Architektur und Chip-Entwurf für Merkmalsdetektoren - 15. 3D-Stapeltechnik für den Sehwürfel - 16. Architektur eines Sehwürfels der ersten Generation Vgl.: https://link.springer.com/book/10.1007%2F978-3-642-00191-8.

Field

Informatik
Kognitionswissenschaft

RSWK

Visuelles System / Synapse / Informationsverarbeitung / Objekterkennung / Merkmalsextraktion / Neuronales Feld

BK

54.72 (Künstliche Intelligenz)

DDC

006.32 / DDC22ger
006.37 / DDC22ger

GHBS

TVU (DU)

LCC

QA76.87

RVK

ST 300
ST 301

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