Automated segmentation of neural recordings for optimal on-line recognition of neural waveforms

Bankman, Isaac N.; Menkes, Alex

doi:10.1109/iembs.1992.5761587

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The threshold was determined by breaking the record into pure neuronal noise segments and setting the threshold at three standard deviations of that noise [32]. The neuronal noise was segmented by an algorithm which separates signal from noise based on the Gaussian characteristics of the noise [33]. Detected spikes were clustered together to form noise-free templates using a recently developed simultaneous clustering algorithm [34]- [36].…”

Section: A Data Collection and Spike Detectionmentioning

confidence: 99%

Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network

Chandra

Optican

1997

IEEE Trans. Biomed. Eng.

105

View full text Add to dashboard Cite

Abstract-Determination of single-unit spike trains from multiunit recordings obtained during extracellular recording has been the focus of many studies over the last two decades. In multiunit recordings, superpositions can occur with high frequency if the firing rates of the neurons are high or correlated, making superposition resolution imperative for accurate spike train determination. In this work, a connectionist neural network (NN) was applied to the spike sorting challenge. A novel training scheme was developed which enabled the NN to resolve some superpositions using single-channel recordings. Simulated multiunit spike trains were constructed from templates and noise segments that were extracted from real extracellular recordings. The simulations were used to determine the performances of the NN and a simple matched template filter (MTF), which was used as a basis for comparison. The network performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. An on-line, real-time implementation of the NN discriminator, using a highspeed digital signal processor mounted inside an IBM-PC, is now in use in six laboratories.

show abstract

Section: A Data Collection and Spike Detectionmentioning

confidence: 99%