Spike sorting relies on the ability to establish the temporal occurrence of action potentials and their relation to specific neurons. Neural information is intrinsically compressible and as such suitable for sparse sampling. Potentially, this should allow for the use of multi-channel recordings, which is particularly advantageous to improve spike sorting. In this paper we propose a novel algorithm capable of sampling neural data at sub-Nyquist rates, yielding the same performance for spike sorting as traditional schemes.
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