A totally automated system for the detection and classification of neural spikes

A system for neural spike detection and classification is presented which does not require a priori assumptions about spike shape or timing. The system is divided into two parts: a learning subsystem and a real-time detection and classification subsystem. The learning subsystem, comprising of feature learning phase and a template learning phase, extracts templates for each separate spike class. The real-time detection and classification subsystems identifies spikes in the noisy neural trace and sorts them into classes, based on the templates and the statistics of the background noise. Comparisons are made among three different schemes for the real-time detection and classification subsystem. Performance of the system is illustrated by using it to classify spikes in segments of neural activity recorded from monkey motor cortex and from guinea pig and ferret auditory cortexes.