Automatic detection of bursts in spike trains recorded from the thalamus of a monkey performing wrist movements.

摘要: In a previous paper (Churchward PR, Butler EG, Finkelstein DI, Aumann TD, Sudbury A, Horne MK. J Neurosci Methods 1997;76:203-210), we showed that simple back propagation neural network could reliably model visual inspection by human observers in detecting the point of change neuronal discharge patterns. The data for study was deliberately chosen so readily detected and there would be high concordance between observers. We wished to extend this investigation comparing variety automatic analysis methods on more complex sets. Two have been discussed paper. knowledge based spike train (KBSTA) designed emulate detection bursts self-organizing feature map (SOFM) determined burst classifying patterns discharge. Neuronal recorded from motor thalamus nucleus ventralis posterior lateralis caudalis (VPLc) monkey performing consecutive trials skilled wrist movements. Recordings were made 36 neurons whose related movement. Three hundred sixty performed during recording these at random used compare three methods, KBSTA, SOFM, inspection. main results show 360 very similar onset offset bursts. SOFM method is not best first approach burst, but it does provides independent evidence support KBSTA methods. conclusion propose as practical, technique identify