A mechanism for the emergence of low-dimensional structures in brain dynamics
Bibliographic record
Abstract
Recent neuroimaging advancements have led to datasets characterized by an overwhelming number of features. Different dimensionality reduction techniques have been employed to uncover low-dimensional manifold representations underlying cognitive functions, while maintaining the fundamental characteristics of the data. These range from linear algorithms to more intricate non-linear methods for manifold extraction. However, the mechanisms responsible for the emergence of these simplified architectures remain a topic of debate. Motivated by concepts from dynamical systems theory, such as averaging and time-scale separation, our study introduces a novel mechanism for the collapse of high dimension brain dynamics onto lower dimensional manifolds. In our framework, fast neuronal activity oscillations average out over time, leading to the resulting dynamics approximating task-related processes occurring at slower time scales. This leads to the emergence of low-dimensional solutions as complex dynamics collapse into slow invariant manifolds. We test this assumption via neural simulations using a simplified model and then enhance the complexity of our simulations by incorporating a large-scale brain network model to mimic realistic neuroimaging signals. We observe in the different cases the convergence of fast oscillatory fluctuations of neuronal activity across time scales that correspond to simulated behavioral configurations. Specifically, by employing various dimensionality reduction techniques and manifold extraction schemes, we observe the reduction of high-dimensional dynamics onto lower-dimensional spaces, revealing emergent low-dimensional solutions. Our findings shed light on the role of frequency and time-scale separation in neuronal activity, proposing and testing a novel theoretical framework for understanding the inner mechanisms governing low-dimensional pattern formation in brain dynamics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".