Linked Life Data

15020095

Source:http://linkedlifedata.com/resource/pubmed/id/15020095

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2004-3-15
pubmed:abstractText
The problem of understanding how ensembles of neurons code for somatosensory information has been defined as a classification problem: given the response of a population of neurons to a set of stimuli, which stimulus generated the response on a single-trial basis? Multivariate statistical techniques such as linear discriminant analysis (LDA) and artificial neural networks (ANNs), and different types of preprocessing stages, such as principal and independent component analysis, have been used to solve this classification problem, with surprisingly small performance differences. Therefore, the goal of this project was to design a new method to maximize computational efficiency rather than classification performance. We developed a peri-stimulus time histogram (PSTH)-based method, which consists of creating a set of templates based on the average neural responses to stimuli and classifying each single trial by assigning it to the stimulus with the 'closest' template in the Euclidean distance sense. The PSTH-based method is computationally more efficient than methods as simple as linear discriminant analysis, performs significantly better than discriminant analyses (linear, quadratic or Mahalanobis) when small binsizes are used (1 ms) and as well as LDA with any other binsize, is optimal among other minimum-distance classifiers and can be optimally applied on raw neural data without a previous stage of dimension reduction. We conclude that the PSTH-based method is an efficient alternative to more sophisticated methods such as LDA and ANNs to study how ensemble of neurons code for discrete sensory stimuli, especially when datasets with many variables are used and when the time resolution of the neural code is one of the factors of interest.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0165-0270
pubmed:author
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
135
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
107-20
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
PSTH-based classification of sensory stimuli using ensembles of single neurons.
pubmed:affiliation
School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S.