title: A neural network-based spike discriminator.
abstract: A software routine to reconstruct individual spike trains from multi-neuron, single-channel extracellular recordings was designed. Using a neural network algorithm that automatically clusters and sorts the spikes, the only user input needed is the threshold level for spike detection and the number of unit types present in the recording. Adaptive features are included in the algorithm to allow for tracking of spike trains during periods of amplitude variation and also to identify noise spikes. The routine will operate on-line during extracellular studies of the cochlear nucleus in cats.
authors: Department of Neurophysiology, University of Wisconsin Medical School, Madison 53706.
authors: J S
authors: JS
authors: Oghalai
authors: W N
authors: WN
authors: Street
authors: W S
authors: WS
authors: Rhode
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D002415:Cats; D017626:Cochlear Nucleus / Q000502:physiology; D004594:Electrophysiology / Q000379:methods*; D005069:Evaluation Studies as Topic; D016571:Neural Networks, Computer