Hardware-Efficient, On-the-Fly, On-Implant Spike Sorter Dedicated to Brain-Implantable Microsystems
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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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 85$ </tex-math></inline-formula> % (unsupervised) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 92$ </tex-math></inline-formula> % (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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.74~\mu \text{W}$ </tex-math></inline-formula> /channel and per-channel area of 0.047 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it