Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Clustering of stored memories in an attractor network with local competition
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2792-1622
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2358-7815
2006 (English)In: International Journal of Neural Systems, ISSN 0129-0657, Vol. 16, no 6, 393-403 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we study an attractor network with units that compete locally for activation and we prove that a reduced version of it has fixpoint dynamics. An analysis, complemented by simulation experiments, of the local characteristics of the network's attractors with respect to a parameter controlling the intensity of the local competition is performed. We find that the attractors are hierarchically clustered when the parameter of the local competition is changed

Place, publisher, year, edition, pages
2006. Vol. 16, no 6, 393-403 p.
Keyword [en]
Attractor neural network, Hierarchical clustering, Hypercolumn, Local competition, Memory clustering
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-6239DOI: 10.1142/S0129065706000809ISI: 000243806000001PubMedID: 17285686Scopus ID: 2-s2.0-33846695887OAI: oai:DiVA.org:kth-6239DiVA: diva2:10891
Note
QC 20100902Available from: 2006-10-09 Created: 2006-10-09 Last updated: 2011-09-21Bibliographically approved
In thesis
1. An Attractor Memory Model of Neocortex
Open this publication in new window or tab >>An Attractor Memory Model of Neocortex
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis presents an abstract model of the mammalian neocortex. The model was constructed by taking a top-down view on the cortex, where it is assumed that cortex to a first approximation works as a system with attractor dynamics. The model deals with the processing of static inputs from the perspectives of biological mapping, algorithmic, and physical implementation, but it does not consider the temporal aspects of these inputs. The purpose of the model is twofold: Firstly, it is an abstract model of the cortex and as such it can be used to evaluate hypotheses about cortical function and structure. Secondly, it forms the basis of a general information processing system that may be implemented in computers. The characteristics of this model are studied both analytically and by simulation experiments, and we also discuss its parallel implementation on cluster computers as well as in digital hardware.

The basic design of the model is based on a thorough literature study of the mammalian cortex’s anatomy and physiology. We review both the layered and columnar structure of cortex and also the long- and short-range connectivity between neurons. Characteristics of cortex that defines its computational complexity such as the time-scales of cellular processes that transport ions in and out of neurons and give rise to electric signals are also investigated. In particular we study the size of cortex in terms of neuron and synapse numbers in five mammals; mouse, rat, cat, macaque, and human. The cortical model is implemented with a connectionist type of network where the functional units correspond to cortical minicolumns and these are in turn grouped into hypercolumn modules. The learning-rules used in the model are local in space and time, which make them biologically plausible and also allows for efficient parallel implementation. We study the implemented model both as a single- and multi-layered network. Instances of the model with sizes up to that of a rat-cortex equivalent are implemented and run on cluster computers in 23% of real time. We demonstrate on tasks involving image-data that the cortical model can be used for meaningful computations such as noise reduction, pattern completion, prototype extraction, hierarchical clustering, classification, and content addressable memory, and we show that also the largest cortex equivalent instances of the model can perform these types of computations. Important characteristics of the model are that it is insensitive to limited errors in the computational hardware and noise in the input data. Furthermore, it can learn from examples and is self-organizing to some extent. The proposed model contributes to the quest of understanding the cortex and it is also a first step towards a brain-inspired computing system that can be implemented in the molecular scale computers of tomorrow.

The main contributions of this thesis are: (i) A review of the size, modularization, and computational structure of the mammalian neocortex. (ii) An abstract generic connectionist network model of the mammalian cortex. (iii) A framework for a brain-inspired self-organizing information processing system. (iv) Theoretical work on the properties of the model when used as an autoassociative memory. (v) Theoretical insights on the anatomy and physiology of the cortex. (vi) Efficient implementation techniques and simulations of cortical sized instances. (vii) A fixed-point arithmetic implementation of the model that can be used in digital hardware.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. ix,148 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2006:14
Keyword
Attractor Neural Networks, Cerebral Cortex, Neocortex, Brain Like Computing, Hypercolumns, Minicolumns, BCPNN, Parallel Computers, Autoassociative Memory
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-4136 (URN)91-7178-461-6 (ISBN)
Public defence
2006-10-26, F2, Lindstedtsvägen 28, Stockholm, 10:15
Opponent
Supervisors
Note
QC 20100903Available from: 2006-10-09 Created: 2006-10-09 Last updated: 2010-09-03Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMedScopus

Authority records BETA

Ekeberg, Örjan

Search in DiVA

By author/editor
Johansson, ChristopherEkeberg, ÖrjanLansner, Anders
By organisation
Computational Biology, CB
In the same journal
International Journal of Neural Systems
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 82 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf