Change search
ReferencesLink to record
Permanent link

Direct link
A Top-Down Approach for a Synthetic Autobiographical Memory System
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
Show others and affiliations
2015 (English)In: BIOMIMETIC AND BIOHYBRID SYSTEMS, LIVING MACHINES 2015, Springer, 2015, 280-292 p.Conference paper (Refereed)
Abstract [en]

Autobiographical memory (AM) refers to the organisation of one's experience into a coherent narrative. The exact neural mechanisms responsible for the manifestation of AM in humans are unknown. On the other hand, the field of psychology has provided us with useful understanding about the functionality of a bio-inspired synthetic AM (SAM) system, in a higher level of description. This paper is concerned with a top-down approach to SAM, where known components and organisation guide the architecture but the unknown details of each module are abstracted. By using Bayesian latent variable models we obtain a transparent SAM system with which we can interact in a structured way. This allows us to reveal the properties of specific sub-modules and map them to functionality observed in biological systems. The top-down approach can cope well with the high performance requirements of a bio-inspired cognitive system. This is demonstrated in experiments using faces data.

Place, publisher, year, edition, pages
Springer, 2015. 280-292 p.
, Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 9222
Keyword [en]
Synthetic autobiographical memory, Hippocampus, Robotics, Deep Gaussian process, MRD
National Category
Computer Science
URN: urn:nbn:se:kth:diva-177974DOI: 10.1007/978-3-319-22979-9_28ISI: 000364183200028ScopusID: 2-s2.0-84947125286ISBN: 978-3-319-22979-9; 978-3-319-22978-2OAI: diva2:875974
4th International Conference on Biomimetic and Biohybrid Systems (Living Machines), JUL 28-31, 2015, Barcelona, SPAIN

QC 20151202

Available from: 2015-12-02 Created: 2015-11-30 Last updated: 2015-12-02Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ek, Carl Henrik
By organisation
Computer Vision and Active Perception, CVAP
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 25 hits
ReferencesLink to record
Permanent link

Direct link