mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Artificial Grammar Recognition Using Spiking Neural Networks
Mälardalen University, School of Innovation, Design and Engineering.
2009 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

This thesis explores the feasibility of Artificial Grammar (AG) recognition using spiking neural networks. A biologically inspired minicolumn model is designed as the base computational unit. Two network topographies are defined with different ideologies. Both networks consists of minicolumn models, referred to as nodes, connected with excitatory and inhibitory connections. The first network contains nodes for every bigram and trigram producible by the grammar’s finite state machine (FSM). The second network has only nodes required to identify unique internal states of the FSM. The networks produce predictable activity for tested input strings. Future work to improve the performance of the networks is discussed. The modeling framework developed can be used by neurophysiological research to implement network layouts and compare simulated performance characteristics to actual subject performance.

Place, publisher, year, edition, pages
2009. , 43 p.
Keyword [en]
Artificial grammar recognition, spiking neural networks, minicolumn model, NEST, Reber grammar
National Category
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-5875OAI: oai:DiVA.org:mdh-5875DiVA: diva2:217254
Presentation
2009-04-15, Gamma, Mälardalen Hogskola, Västerås, 01:50 (English)
Uppsok

Supervisors
Examiners
Available from: 2009-05-14 Created: 2009-05-13 Last updated: 2009-05-14Bibliographically approved

Open Access in DiVA

fulltext(669 kB)614 downloads
File information
File name FULLTEXT01.pdfFile size 669 kBChecksum SHA-512
ed8c6a4c46289d4a4fe5c2ef13529a3bfacc376ec089130c4db6a5f24f48fe452da217423c80018379e991a01eab7ced150f6b046dcba34e0c82684176311500
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Cavaco, Philip
By organisation
School of Innovation, Design and Engineering
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 614 downloads
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

Total: 128 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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