A Fully Automated Approach to Spike Sorting

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.

/pdf/an-online-spike-detection-and-spike-classification-algorithm-506rf6vadl.pdf

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

Significant strides have been recently made to develop highly sensorized cybernetic prostheses aimed at restoring sensorimotor limb functions to those who have lost them because of a traumatic event (amputation). In these cases, one of the main goals is to create a bidirectional link between the artificial devices (e.g., robotic hands, arms, or legs) and the nervous system. Several human-machine interfaces (HMIs) are currently used to this aim. Among them, interfaces with the peripheral nervous system and in particular longitudinal intrafascicular electrodes can be a promising solution able to improve the current situation. In this paper, the potentials and limits of the use of this interface to control robotic devices are presented. Specific information is provided on: 1) the neurophysiological bases for the use peripheral nerve interfaces; 2) a comparison of the potentials of the different peripheral neural interfaces; 3) the possibility of extracting and appropriately interpreting the neural code for motor commands and of delivering sensory feedback by stimulating afferent fibers by using longitudinal intrafascicular electrodes; 4) a preliminary comparative analysis of the performance of this approach with the ones of others HMIs; 5) the open issues which have to be addressed for a chronic usability of this approach.

On the Use of Longitudinal Intrafascicular Peripheral Interfaces for the Control of Cybernetic Hand Prostheses in Amputees

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

/pdf/a-model-based-spike-sorting-algorithm-for-removing-1dn5dk9ubb.pdf

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

How many neurons can we see with current spike sorting algorithms

https://nip.sjtu.edu.cn/pdf/Spike%20sorting%20based%20on%20automatic%20template%20reconstruction%20with%20a%20partial%20solution.pdf

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem

Spike sorting is a prerequisite for studying the multi-channel extracellular recordings. A new method is developed for spike sorting in multi-channel extracellular recordings of retinas. Based on the assumption that noise follows Gaussian distribution, spike templates are constructed based on principal component analysis and subtractive clustering, then a template matching procedure is applied to choose the best-fit template or templates combination for overlapping spikes so that after subtracting the template or templates combination from the spike event, the sum of squared residue follows Chi-square distribution. Artificial spike trains are used to assess the performance of the proposed method.

/pdf/spike-sorting-in-multi-channel-extracellular-recordings-of-2qe4fptjl6.pdf

Jin-Yong Wu

Papers

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem

Spike sorting in multi-channel extracellular recordings of retinas