A Totally Automated Neural Spike Detection and Classification Scheme: A Preliminary Software System.
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A system for neural spike detection and classification is presented, which does not require a priori assumptions about spike presence or spike templates, and assumes only that the background noise has a Gaussian distribution. The system is divided into two parts: a learning subsystem and a real-time detection and classification subsystem. The former extracts templates of spikes for every class which includes a feature learning phase and a template learning phase. The latter picks up spikes in the noisy trace and sorts them out into classes, based on the templates that the learning subsystem provides and the statistics of the background noise. Performance of the system is illustrated by using it to classify spikes in a segment of neural activity recorded from monkey motor cortex. The system is implemented without human supervision so that it can be extended for multi-channel recording without loss of real-time property.