Paper Info
Title
Unsupervised recognition of neuronal discharge waveforms for on-line real-time operation
Abstract
Fast and reliable unsupervised spike sorting is necessary for electrophysiological applications that require critical time operations (e.g., recordings during human neurosurgery) or management of large amount of data (e.g., recordings from large microelectrode arrays in behaving animals). We present an algorithm that can recognize the waveform of neural traces corresponding to extracellular action potentials. Spike shapes are expressed in a phase space spanned by the first and second derivatives of the raw signal trace. The performance of the algorithm is tested against artificially generated noisy data sets. We present the main features of the algorithm aimed to on-line real-time operations.
Year
DOI
Venue
2005
10.1007/11565123_3
BVAI
Keywords
Field
DocType
on-line real-time operation,main feature,large microelectrode array,noisy data set,action potential,reliable unsupervised spike,large amount,spike shape,human neurosurgery,unsupervised recognition,electrophysiological application,neuronal discharge waveform,critical time operation,microelectrode array,phase space
Signal trace,Spike sorting,Pattern recognition,Computer science,Signal-to-noise ratio,Waveform,Phase space,Sorting,Artificial intelligence,Independent component analysis,Electrophysiology,Distributed computing
Conference
Volume
ISSN
ISBN
3704
0302-9743
3-540-29282-9
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Yoshiyuki Asai1307.56
Tetyana I. Aksenova211.06
Alessandro E . P. Villa334853.26