Modulation of Neuronal Intrinsic Excitabilities to Enhance Network Resilience using Electrical Stimulation
Bibliographic record
Abstract
Current evidence suggests that reduced diversity in neuronal biophysical properties is associated with heightened network synchrony in seizure-prone tissue. We hypothesized that targeted multi-electrode stimulation (neuromodulation) could modulate intrinsic excitability to broaden the distribution of neuronal firing rates and reduce synchrony. To test this, we developed OpenMEA—a novel, open-source and modular microelectrode array (MEA) platform capable of simultaneously recording from 60 channels at 16 kHz and delivering programmable stimulation patterns. Using this system, we applied theta-burst stimulation (TBS) in two paradigms: (1) synchronized stimulation across all electrodes to homogenize population firing rates, and (2) randomized stimulation with variable parameters and electrode subsets to diversify them. As proof of concept, we show that our stimulation protocols can bidirectionally modulate firing rate variance, a proxy for intrinsic excitability, across neuronal populations. These findings establish the feasibility of using OpenMEA for closed-loop neuromodulation experiments and lay the groundwork for future studies on preventing hypersynchrony in cortical networks.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".