Browse all publications by topic
Browse all publications by year
- F. Franke, M. Natora,
C. Boucsein, M. Munk, and K. Obermayer. An online spike detection and
spike classification algorithm capable of instantaneous resolution of
overlapping spikes.
.
Journal of Computational Neuroscience, 2009.
in press.
(FTP PDF, 5680 kb)
For the analysis of neuronal cooperativity, simultaneously recorded
extracellular signals from neighboring neurons need to be sorted reliably by
a spike sorting method. Many algorithms have been developed to this end,
however, to date, none of them manages to fulfill a set of demanding
requirements. In particular, it is desirable to have an algorithm that
operates online, detects and classifies overlapping spikes in real time, and
that adapts to non-stationary data. Here, we present a combined spike
detection and classification algorithm, which explicitly addresses these
issues. Our approach makes use of linear filters to find a new representation
of the data and to optimally enhance the signal-to-noise ratio. We introduce
a method called “Deconfusion” which de-correlates the filter outputs and
provides source separation. Finally, a set of well-defined thresholds is
applied and leads to simultaneous spike detection and spike classification.
By incorporating a direct feedback, the algorithm adapts to non-stationary
data and is, therefore, well suited for acute recordings. We evaluate our
method on simulated and experimental data, including simultaneous
intra/extra-cellular recordings made in slices of a rat cortex and recordings
from the prefrontal cortex of awake behaving macaques. We compare the results
to existing spike detection as well as spike sorting methods. We conclude
that our algorithm meets all of the mentioned requirements and outperforms
other methods under realistic signal-to-noise ratios and in the presence of
overlapping spikes.
|
