Operant Conditioning of Single Neuron Activity in Brain-Machine Interfaces
Bibliographic record
Abstract
Invasive brain-machine interfaces (BMIs) have the potential to restore lost motor function due to paralysis. However, successful operation of BMIs depends on the extent to which neural activity can be volitionally modulated. Operant conditioning of single cortical neurons induces the rapid acquisition of arbitrary associations between machines and neural activity. In this thesis, I aimed at exploring intrinsic and extrinsic factors that influence single neuron behavior in a BMI, guided by operant conditioning in a rat model. Extracellularly recorded single neuron activity, from layer V of the motor cortex, was conditioned in BMI tasks designed to up-regulate instantaneous or smoothed firing rates. In the first two studies (Chapters 3 4), the neuron type was investigated as an intrinsic factor. Differences were found in the neuron-type-specific utility (defined as the degree of activity up-regulation), and in the neuron-type-specific responses in the activity leading to a reward. Next, I investigated the type of BMI task as an extrinsic factor (Chapter 5). Two tasks were compared: (i) a threshold-based task, in which firing rates surpassed a threshold, and (ii) a graded-activation task, where firing rates were maintained within a narrow window of activation. Single neuron activity was selectively up-regulated in the threshold-based task, while the graded activation task involved activation of neurons in the local network. This thesis demonstrates that intrinsic and extrinsic factors have an influence in the behavior of single neurons, and that these factors might inform the design of BMIs.
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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".