Recent technical advancements in neural engineering allow for precise recording and control of neural circuits simultaneously, opening up new opportunities for closed-loop neural control. In this work, a rapid spike sorting system was developed based on template matching to rapidly calculate instantaneous firing rates for each neuron in a multi-unit extracellular recording setting. Cluster templates were first generated by a desktop computer using a non-parameter spike sorting algorithm (Super-paramagnetic clustering) and then transferred to a field-programmable gate array digital circuit for rapid sorting through template matching. Two different matching techniques–Euclidean distance (ED) and correlational matching (CM)–were compared for the accuracy of sorting and the performance of calculating firing rates. The performance of the system was first verified using publicly available artificial data and was further confirmed with pre-recorded neural spikes from an anesthetized Mongolian gerbil. Real-time recording and sorting from an awake mouse were also conducted to confirm the system performance in a typical behavioral neuroscience experimental setting. Experimental results indicated that high sorting accuracies were achieved for both template-matching methods, but CM can better handle spikes with non-Gaussian spike distributions, making it more robust for in vivo recording. The technique was also compared to several other off-line spike sorting algorithms and the results indicated that the sorting accuracy is comparable but sorting time is significantly shorter than these other techniques. A low sorting latency of under 2 ms and a maximum spike sorting rate of 941 spikes/second have been achieved with our hybrid hardware/software system. The low sorting latency and fast sorting rate allow future system developments of neural circuit modulation through analyzing neural activities in real-time.

Superparamagnetic clustering (SPC) is an unsupervised classification technique in which clusters are self-organised based on data density and mutual interaction energy. Traditional SPC algorithm uses the Swendsen-Wang Monte Carlo approximation technique to significantly reduce the search space for reasonable clustering. However, Swendsen-Wang approximation is a Markov process which limits the conventional superparamagnetic technique to process data clustering in a sequential manner. Here the authors propose a parallel approach to replace the conventional appropriation to allow the algorithm to perform clustering in parallel. One synthetic and one open-source dataset were used to validate the accuracy of this parallel approach in which comparable clustering results were obtained as compared to the conventional implementation. The parallel method has an increase of clustering speed at least 8.7 times over the conventional approach, and the larger the sample size, the more increase in speed was observed. This can be explained by the higher degree of parallelism utilised for the increased data points. In addition, a hardware architecture was proposed to implement the parallel superparamagnetic algorithm using digital electronic technologies suitable for rapid or real-time neural spike sorting.

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