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- R. Vollgraf, M. Munk, and
K. Obermayer. Spike Sorting of Multi-Site Electrode Recordings.
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Network - Computation in Neural Systems, 16:85-113, 2005.
We derive an optimal linear filter, in order to reduce the
distortions of the peak amplitudes of action potentials in extra-cellular
multitrode recordings, which are due to background activity and overlapping
spikes. This filter is learned very efficiently from the raw recordings in an
unsupervised manner, and responds to the average wave form with an impulse of
minimal width. The average wave form does not have to be known in advance,
but is learned together with the optimal filter. The peak amplitude of a
filtered wave form is a more reliable estimate for the amplitude of an action
potential than the peak of the biphasic wave form and can improve the
accuracy of the event detection and clustering procedures. We demonstrate a
spike sorting application, in which events are detected using the Mahalanobis
distance in the N-dimensional space of filtered recordings as a distance
measure, and the event amplitudes of the filtered recordings are clustered in
order to assign events to individual units. This method is fast and robust,
and we show its performance by applying it to real tetrode recordings of
spontaneous activity in the visual cortex of an anesthetized cat and to
realistic artificial data derived therefrom.
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