English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Modelling Spikes with Mixtures of Factor Analysers

Görür, D., Rasmussen, C., Tolias, A., Sinz, F., & Logothetis, N. (2004). Modelling Spikes with Mixtures of Factor Analysers. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 391-398). Berlin, Germany: Springer.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D7ED-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-C563-C
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Görür, D1, 2, Author              
Rasmussen, CE1, 2, Author              
Tolias, AS2, 3, Author              
Sinz, F1, 2, Author              
Logothetis, NK2, 3, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

Content

show
hide
Free keywords: -
 Abstract: Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging problem. We consider the spike sorting problem using a generative model,mixtures of factor analysers, which concurrently performs clustering and feature extraction. The most important advantage of this method is that it quantifies the certainty with which the spikes are classified. This can be used as a means for evaluating the quality of clustering and therefore spike isolation. Using this method, nearly simultaneously occurring spikes can also be modelled which is a hard task for many of the spike sorting methods. Furthermore, modelling the data with a generative model allows us to generate simulated data.

Details

show
hide
Language(s):
 Dates: 2004-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-28649-3_48
BibTex Citekey: 2646
 Degree: -

Event

show
hide
Title: 26th Annual Symposium of the German Association for Pattern Recognition (DAGM 2004)
Place of Event: Tübingen, Germany
Start-/End Date: 2004-08-30 - 2004-09-01

Legal Case

show

Project information

show

Source 1

show
hide
Title: Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004
Source Genre: Proceedings
 Creator(s):
Rasmussen, CE1, Editor            
Bülthoff, HH1, Editor            
Schölkopf, B1, Editor            
Giese, MA, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 391 - 398 Identifier: ISBN: 978-3-540-22945-2

Source 2

show
hide
Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 3175 Sequence Number: - Start / End Page: - Identifier: -