15.5 Event-Based Spatially Zooming Neural Interface IC with 10nW/Input Reconfigurable-Inverter Fabric and Input-Adaptive Quantization
Bibliographic record
Abstract
Large-scale neural interface ICs have tens of thousands of electrodes [1]–[3], enabling a wide range of applications including neural prostheses and therapeutic neuromodulation. However, the human brain contains 86 billion neurons and new frontiers in brain interfacing, such as understanding memory and cognition, will benefit from concurrent access to a million or more of implanted electrodes [4]. Modern microfabrication technologies, including silicon wafer thinning [1], allow for dense co-integration of electrodes and transistors on the same flexible substrate [1,4-6] and overcome the issue of the mega-scale electrode interconnect bottleneck [7]. The key remaining challenges are the low energy efficiency of neural ADCs and the high output data rate [4]. For example, one million inputs require 10nW/input ADC power - for a tissue-safe 10mW ADC total power budget [8], and a 200Gb/s wireless link - for 8b conversion at 25kHz [4]. However, the power dissipation of neural ADCs, either dedicated [9]–[12] or time-multiplexed [1-3,13], is over 50× higher, and implantable radios are at least 100× slower [14]–[15]. To address these challenges, neural spiking sparsity has been exploited in both off-line [16]–[17] and on-line [8], [18] methods of optimum electrode selection, but this leads to significant losses in recorded information [17] and requires$\text{near}-\cup \mathrm{W}/\text{inp}\lfloor\vert \mathrm{t}$power due to static circuit biasing [8], [18], respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".