Introduction to the HBM NeuroAI course 2023: artificial neural networks as models of the brain in cognitive neuroscience
Bibliographic record
Abstract
This is a short introduction to a one-day course on neuroAI models for cognitive neuroscience. A detailed description of the course can be found on the following website: https://neuroai-educational.github.io The topics covered in the course are as follows: <strong>Technical construction of the brain models</strong>: An introduction of fundamental concepts applicability of AI in the neuroscience research; <strong>Brain decoding and encoding</strong>: Identifying cognitive states based on brain activity (<strong>brain decoding</strong>); and predicting brain activity based on the activity of an artificial neural network (<strong>brain encoding</strong>); <strong>Multimodal overview of NeuroAI</strong>: Implementation of AI in various sensory modality processing including vision, auditory, language. <strong>Ethics and future of NeuroAI</strong>: Ethical concerns and potential future directions in the field.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".