Hardware-Efficient, On-the-Fly, On-Implant Spike Sorter Dedicated to Brain-Implantable Microsystems
Bibliographic record
Abstract
This article proposes an unsupervised online spike sorter, dedicated to brain-implantable neural recording microsystems. The main (online) spike sorting phase in the proposed approach is based on the wave shape resemblance between spike classes, realized by template matching. This phase follows an offline training phase, implemented off the implant. In the training phase, the number and centroids of the clusters are automatically determined and subsequently sent to the implant to configure the on-implant online spike sorter. Comprehensively verified using two separate datasets with a wide spectrum of spike wave shapes, the proposed approach presents average classification accuracies of$\sim 85$% (unsupervised) and$\sim 92$% (supervised). A 64-channel spike sorter was designed using a computational core with folded architecture. To make the very large-scale integration (VLSI) implementation of this spike sorter appropriate for brain implants in terms of both power and area consumption, the computations realizing the proposed approach were significantly reduced. Designed in a standard 180-nm CMOS technology, the circuit consumes$1.74~\mu \text{W}$/channel and per-channel area of 0.047 mm2. The circuit is capable of clustering neural spikes in real-time with a latency of as short as 1.36 ms. A prototype of the circuit was implemented and successfully tested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".