An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming

Abstract

In the case of extracellular recordings, spike detection algorithms are necessary in order to retrieve information about neuronal activity from the data. We present a new spike detection algorithm which is based on methods from the field of blind equalization and beamforming and which is particularly adapted to the specific signal structure neuronal data exhibit. In contrast to existing approaches, our method blindly estimates several waveforms directly from the data, selects automatically an appropriate detection threshold, and is also able to track neurons by filter adaptation. The few parameters of the algorithm are biologically motivated, thus, easy to set. We compare our method with current state-of-the-art spike detection algorithms and show that the proposed method achieves favorable results. Realistically simulated data as well as data acquired from simultaneous intra/extracellular recordings in rat slices are used as evaluation datasets.

Spike detection and spike sorting with a hidden Markov model improves offline decoding of motor cortical recordings

“…Even though this approach is computationally intensive (section 3.3), our results suggest that directly looking for an action potential waveform shape in the continuous voltage time series is better for spike detection than approaches we compared against. Conceptually, using a matched filter [11,54,[74][75][76] is very similar to our approach in that it looks for the waveform shape directly. However, our analysis of the matched filter found that it did not perform as well as the HMM, though more recent work [77] may address the shortcomings of matched filters.…”