An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

Abstract

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.

A unified framework and method for automatic neural spike identification

“…If the change in shape is small relative to the noise, and is still closer to the original waveform than to the waveform of any other cell (or linear combination of cells), then the thresholding post-processing step is likely to identify the correct waveform. For slow drifts in waveform shape, one could apply the method separately to temporally restricted portions of the data, re-estimating the waveform shapes for each (Franke et al, 2009). A more elegant solution would be to develop an incremental version of the algorithm that operates on voltage measurements as they arrive, rather than operating simultaneously on the full data set.…”

“…However, these methods generally rely on brute-force examination of all combinations of spike waveforms at all time separations (impractical for simultaneous recordings of many cells), or "greedy" algorithms that iteratively subtract the waveform of the best-fitting cell until the residual amplitude is within the range expected for noise. A notable exception is the family of ICA-based spike sorting methods (Takahashi et al, 2003;Takahashi and Sakurai, 2005;Franke et al, 2009), which bear some resemblance to our approach, but have not been developed or implemented in the context of a unified probabilistic model for the voltage measurements, and have not been extensively tested and compared to traditional clustering methods.…”

Automated spike sorting algorithmbased on Laplacian eigenmaps andk-means clustering

“…In the final step each spike is assigned to the neuron that generated it. Spike detection methods reported in the literature include amplitude thresholding [9][10][11], and nonlinear energy operator [12,13]. In the feature extraction step, parameters are computed from the spike that yields the best discrimination.…”

Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

“…Furthermore, we notice the EI estimation step is essentially spike sorting; therefore, there is room for the use of state-of-the-art [48,49] methods to achieve efficient implementations. This outer loop would be especially helpful to enable the online update of the EI in order to counteract the effect of tissue drift, or to correct possible biases in estimates of the EI provided by visual stimulation [50,51], which could lead to problematic changes in EI shape over the course of an experiment. We acknowledge, however, that the implementation of this loop could significantly increase the computational complexity of our algorithm, and deem as an open problem how to achieve a reduction in computational complexity so that online data analysis would still be feasible.…”