From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits

BACKGROUND

Spike sorting is a basic step for implantable neural interfaces. With the growing number of channels, the process should be computationally efficient, automatic,robust and applicable on implantable circuits.

NEW METHOD

The proposed method is a combination of fully-automatic offline and online processes. It introduces a novel method for automatically determining a data-aware spike detection threshold, computationally efficient spike feature extraction, automatic optimal cluster number evaluation and verification coupled with Self-Organizing Maps to accurately determine cluster centroids. The system has the ability of unsupervised online operation after initial fully-automatic offline training. The prime focus of this paper is to fully-automate the complete spike detection and sorting pipeline, while keeping the accuracy high.

RESULTS

The proposed system is simulated on two well-known datasets. The automatic threshold improves detection accuracies significantly( > 15%) as compared to the most common detector. The system is able to effectively handle background multi-unit activity with improved performance.

COMPARISON

Most of the existing methods are not fully-automatic; they require supervision and expert intervention at various stages of the pipeline. Secondly, existing works focus on foreground neural activity. Recent research has highlighted importance of background multi-unit activity, and this work is amongst the first efforts that proposes and verifies an automatic methodology to effectively handle them as well.

CONCLUSION

This paper proposes a fully-automatic, computationally efficient system for spike sorting for both single-unit and multi-unit spikes. Although the scope of this work is design and verification through computer simulations, the system has been designed to be easily transferable into an integrated hardware form.

Unsupervised and real-time spike sorting chip for neural signal processing in hippocampal prosthesis

BACKGROUND

Damage to the hippocampus will result in the loss of ability to form new long-term memories and cognitive disorders. At present, there is no effective medical treatment for this issue. Hippocampal cognitive prosthesis is proposed to replace damaged regions of the hippocampus to mimic the function of original biological tissue. This prosthesis requires a spike sorter to detect and classify spikes in the recorded neural signal.

NEW METHOD

A 16-channel spike sorting processor is presented in this paper, where all channels are considered as independent. An automatic threshold estimation method suitable for hardware implementation is proposed for the Osort clustering algorithm. A new distance metric is also introduced to facilitate clustering. Bayes optimal template matching classification algorithm is optimized to reduce computational complexity by introducing a preselection mechanism.

RESULTS

The chip was fabricated in 40-nm CMOS process with a core area of 0.0175 mm²/ch and power consumption of 19.0 μW/ch. Synthetic and realistic test data are used to evaluate the chip. The test result shows that it has high performance on both data.

COMPARISON WITH EXISTING METHOD(S)

Compared with the other three spike sorting processors, the proposed chip achieves the highest detection and classification accuracy. It also has the ability to deal with partially overlapping spikes, which is not reported in the other work.

CONCLUSIONS

We have developed a 16-channel spike sorting chip used in hippocampal prosthesis, which provides unsupervised clustering and real-time detection and classification. It also has the ability to deal with partially overlapping spikes.