title: A neural network approach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments.
abstract: A multi-channel, real-time, unsupervised spike discriminator was developed in order to reconstruct single spike trains from several simultaneously recorded multi-unit nerve filaments. The program uses a Self Organising Map (SOM) algorithm for the classification of the spikes. In contrast to previous similar techniques, the described method is made for use on a PC, and the method may thus be implemented at relatively low cost. In order to test the accuracy of the program, a robustness test was performed, where noise with different RMS levels was superimposed on the spikes. Furthermore, the maximal classification rate was determined. The program is easy to use, since the only manual inputs needed are the voltage threshold for spike detection, and the number of units present in each recorded nerve filament.
authors.affiliation: Division of Work Physiology, National Institute of Occupational Health, Umeå, Sweden.
authors.forename: F
authors.initials: F
authors.lastname: Ohberg
authors.forename: H
authors.initials: H
authors.lastname: Johansson
authors.forename: M
authors.initials: M
authors.lastname: Bergenheim
authors.forename: J
authors.initials: J
authors.lastname: Pedersen
authors.forename: M
authors.initials: M
authors.lastname: Djupsjöbacka
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D002415:Cats; D003199:Computer Systems; D004594:Electrophysiology / Q000379:methods*; D016571:Neural Networks, Computer; D009475:Neurons, Afferent / Q000502:physiology*