Evaluation and resolution of many challenges of neural spike-sorting: a new sorter

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth datasets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth datasets and suggest post-sorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.

Characteristic of Neural Signal Feature for Spike Sorting and Detection

Neural spike plays an important role in understanding brain activities, and in neural spike sorting, the features of signal are of great importance. This paper aims to have a review on features used to discriminate different originated spikes. The features are divided into three categories: features in the time domain, features in the transformation domain, and features of dimensional reduction. For each kind of feature, the basic principle, advantages, and disadvantages are described and discussed. Results showed that features in the time domain are suitable for on-chip or real-time spike sorting, while features in the transformation domain can be used in offline spike sorting aiming at high performance. For features of dimensional reduction, it makes a large number of features available in spike sorting. In conclusion, researchers need to determine features by balancing the minimization of calculation complexity and maximizing sorting performance according to different occasions and demands. Expectations are also made for future directions of spike feature studies. The article may guide both the physiologists who want to determine features in neural spike sorting and researchers who want to work on feature extracting algorithms further to achieve better performance in experimental challenges.

Subthalamic nucleus stabilizes movements by reducing neural spike variability in monkey basal ganglia: chemogenetic study

AbstractsThe subthalamic nucleus (STN) projects to the external pallidum (GPe) and internal pallidum (GPi), the relay and output nuclei of the basal ganglia (BG), respectively, and plays an indispensable role in controlling voluntary movements. To elucidate the neural mechanism by which the STN controls GPe/GPi activity and movements, we utilized a chemogenetic method to reversibly suppress the motor subregion of the STN in three macaque monkeys (Macaca fuscata, both sexes) engaged in reaching tasks. Systemic administration of chemogenetic ligands prolonged movement time and increased spike train variability in the GPe/GPi, but only slightly affected firing rate modulations. Across-trial analyses revealed that the irregular discharge activity in the GPe/GPi coincided with prolonged movement time. STN suppression also induced excessive abnormal movements in the contralateral forelimbs, which was preceded by STN and GPe/GPi phasic activity changes. Our results suggest that the STN stabilizes spike trains in the BG and achieves stable movements.