Linked Life Data

17052762

Source:http://linkedlifedata.com/resource/pubmed/id/17052762

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-1-26
pubmed:abstractText
We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0165-0270
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
160
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
52-68
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Spike sorting based upon machine learning algorithms (SOMA).
pubmed:affiliation
Department of Informatics, Sussex University, Brighton BN1 9QH, UK. pmh20@sussex.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't