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Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes

Citation Author(s):
Jasper Wouters, Fabian Kloosterman, Alexander Bertrand
Submitted by:
Jasper Wouters
Last updated:
15 April 2018 - 9:21am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Jasper Wouters
Paper Code:
2939
 

Spike sorting is the process of assigning each detected neuronal spike in an extracellular recording to its putative source neuron. A linear filter design is proposed where the filter output allows for threshold-based spike sorting of high-density neural probe data. The proposed filter design is based on optimizing the signal-to-peak-interference ratio for each detectable neuron in a data-driven way. Threshold-based spike sorting using linear filters is particularly interesting for realtime spike sorting because of the low computational complexity and predictable delay of those filters, enabling closed-loop neuroscience with unit-activity controlled brain stimulation. We validate our method on both paired and hybrid in-vivo recorded high-density data.

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