Functional diversity of visual cortex improves constraint-free natural image reconstruction from human brain activity
Bibliographic record
Abstract
Previous brain decoding studies using functional magnetic resonance imaging (fMRI) have greatly advanced our understanding of human visual coding and non-invasive brain-machine interfaces. However, most of these studies focus on classifying a limited number of image categories or reconstructing visual images with additional information, e.g., semantic categories and textual cues. Constraint-free visual reconstruction remains scarce. Here, we propose a generative network based on the functional diversity of the human visual cortex (FDGen) that takes multivariate brain activity as input and directly reconstructs natural images perceived by observers without any additional cues (semantic categories or textual description). Our FDGen is augmented by two bio-inspired computational modules. Based on the functional specializations of the human visual cortex, we propose a new function-based input module (FIM) that projects responses from different brain regions into separate feature spaces. Second, inspired by human attention, we construct a computational module to derive attentive feature weights at the function level to refine the feature map. These function-selection modules (FSMs) allow the network to dynamically select multiscale visual information during the generation process. We test FDGen on the popular fMRI datasets of natural images and achieve highly robust performance. Our work represents an important step forward in the development of fMRI-based brain decoding algorithms and highlights the utility of neuroscience theories in the design of deep learning models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".