A NEURAL-NETWORK-BASED SPIKE DISCRIMINATOR

A software routine to reconstruct individual spike trains from multi-n euron, single-channel extracellular recordings was designed. Using a n eural network algorithm that automatically clusters and sorts the spik es, the only user input needed is the threshold level for spike detect ion and the number of unit types present in the recording. Adaptive fe atures are included in the algorithm to allow for tracking of spike tr ains 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.