Using Generative and Explainable Neural Networks to Investigate the Relationship Between Motor Cortex Activity and Animal Behavior During Motor Task Learning.
Bibliographic record
Abstract
<title>Abstract</title> Understanding complex relations between neuronal activity and animal behavior is one of the most crucial questions in neuroscience. Rapid advancements in Machine Learning (ML) methods offer new powerful tools that can be used to investigate highly non-linear mapping between motor cortex activity and body movements. Here, by using explainable convolutional network (ConvNet) and Generative Adversarial Networks (GAN), we show how neuronal activity can be predicted from raw videos of animal behavior, and interestingly, we show that detailed videos of behaving animals can be recreated from activity of just few selected neurons. Those analyses revealed that the predictability of behavior from neuronal activity (and vice versa) initially increases as an animal learns a new task. However, after the animal performance on a motor task achieves the required accuracy, then “coupling” between neuronal activity and behavior decreases, without degrading task performance. In summary, we show how new ML methods could be applied to provide new insights in the relation between neuronal activity and behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.004 |
| 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".