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.