Marple: Scalable Spike Sorting for Untethered Brain-Machine Interfacing
Bibliographic record
Abstract
Spike sorting is the process of parsing electrophysiological signals from neurons to identify if, when, and which particular neurons fire. Spike sorting is a particularly difficult task in computational neuroscience due to the growing scale of recording technologies and complexity in traditional spike sorting algorithms. Previous spike sorters can be divided into software-based and hardware-based solutions. Software solutions are highly accurate but operate on recordings after-the-fact, and often require utilization of high-power GPUs to process in a timely fashion, and they cannot be used in portable applications. Hardware solutions suffer in terms of accuracy due to the simplification of mechanisms for implementation's sake and process only up to 128 inputs. This work answers the question: "How much computation power and memory storage is needed to sort spikes from 1000s of channels to keep up with advances in probe technology?" We analyze the computational and memory requirements for modern software spike sorters to identify their potential bottlenecks - namely in the template memory storage. We architect Marple, a highly optimized hardware pipeline for spike sorting which incorporates a novel mechanism to reduce the template memory storage from 8 - 11x. Marple is scalable, uses a flexible vector-based back-end to perform neuron identification, and a fixed-function front-end to filter the incoming streams into areas of interest. The implementation is projected to use just 79mW in 7nm, when spike sorting 10K channels at peak activity. We further demonstrate, for the first time, a machine learning replacement for the template matching stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".