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The Journal of Neurophysiology Vol. 84 No. 1 July 2000, pp. 401-414
Copyright ©2000 by the American Physiological Society
Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey 07102
Harris, Kenneth D.,
Darrell A. Henze,
Jozsef Csicsvari,
Hajime Hirase, and
György Buzsáki.
Accuracy of Tetrode Spike Separation as Determined by
Simultaneous Intracellular and Extracellular Measurements. J. Neurophysiol. 84: 401-414, 2000. Simultaneous
recording from large numbers of neurons is a prerequisite for
understanding their cooperative behavior. Various recording techniques
and spike separation methods are being used toward this goal. However,
the error rates involved in spike separation have not yet been
quantified. We studied the separation reliability of "tetrode"
(4-wire electrode)-recorded spikes by monitoring simultaneously from
the same cell intracellularly with a glass pipette and extracellularly
with a tetrode. With manual spike sorting, we found a trade-off between
Type I and Type II errors, with errors typically ranging from 0 to 30%
depending on the amplitude and firing pattern of the cell, the
similarity of the waveshapes of neighboring neurons, and the experience
of the operator. Performance using only a single wire was markedly
lower, indicating the advantages of multiple-site monitoring techniques
over single-wire recordings. For tetrode recordings, error rates were
increased by burst activity and during periods of cellular synchrony.
The lowest possible separation error rates were estimated by a search
for the best ellipsoidal cluster shape. Human operator performance was
significantly below the estimated optimum. Investigation of error
distributions indicated that suboptimal performance was caused by
inability of the operators to mark cluster boundaries accurately in a
high-dimensional feature space. We therefore hypothesized that
automatic spike-sorting algorithms have the potential to significantly
lower error rates. Implementation of a semi-automatic classification
system confirms this suggestion, reducing errors close to the estimated
optimum, in the range 0-8%.
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