A Reconfigurable Modular Microelectrode Array Platform for Fast Prototyping of Implantable Closed-Loop Neuromodulation Medical Devices
Bibliographic record
Abstract
Developing implantable, closed-loop neuromodulation devices demands seamless integration of neuroscience discovery, algorithm design, and ultra-low-power hardware-an effort that traditionally stretches across many years and multiple specialist teams. Existing commercial or open-source electrophysiology tools do not offer the broad flexibility desired to enable rapid testing of custom stimulation paradigms and closed-loop algorithms, slowing the path from bench-top insight to patient-ready silicon. To address this we present a flexible platform that integrates four interoperable modes: (1) Experimental validation - for neuroscientists to explore new stimulation protocols and test their efficacy; (2) Software development-to enable realtime closed-loop algorithm testing; (3) Hardware development for translation of proven software code into FPGA-accelerated, power-efficient hardware; (4) ASIC validation - for post-silicon performance testing before deployment in chronic studies. This modular architecture enables parallel, cross-functional collaboration, reduces design spins, and lowers entry barriers for labs lacking end-to-end neuromodulation infrastructure. Preliminary in-vitro results from human brain tissue using microelectrode arrays demonstrate reliable operation, validating the system. By streamlining the transition from neuroscience insight to implant-grade ASIC, our system accelerates therapeutic device development and opens new avenues for fundamental investigations of brain function.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".