Linked Life Data

16350435

Source:http://linkedlifedata.com/resource/pubmed/id/16350435

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-12-14
pubmed:abstractText
We derive an optimal linear filter, to reduce the distortions of the peak amplitudes of action potentials in extracellular multitrode recordings, which are due to background activity and overlapping spikes. This filter is being learned very efficiently from the raw recordings in an unsupervised manner and responds to the average waveform with an impulse of minimal width. The average waveform does not have to be known in advance, but is learned together with the optimal filter. The peak amplitude of a filtered waveform is a more reliable estimate for the amplitude of an action potential than the peak of the biphasic waveform and can improve the accuracy of the event detection and clustering procedures. We demonstrate a spike-sorting application, in which events are detected using the Mahalanobis distance in the N-dimensional space of filtered recordings as a distance measure, and the event amplitudes of the filtered recordings are clustered to assign events to individual units. This method is fast and robust, and we show its performance by applying it to real tetrode recordings of spontaneous activity in the visual cortex of an anaesthetized cat and to realistic artificial data derived therefrom.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0954-898X
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
85-113
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Optimal filtering for spike sorting of multi-site electrode recordings.
pubmed:affiliation
Berlin University of Technology, Neural Information Processing, Berlin, Germany. vro@cs.tu-berlin.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't