Emergence of structures in neuronal network activities
Bibliographic record
Abstract
Nonlinear responses of individual neurons are both experimentally established and considered fundamental for the functioning of neuronal circuitry. Consequently, one may envisage the collective dynamics of large networks of neurons exhibiting a large repertoire of nonlinear behaviors. However, an ongoing and central challenge in the modeling of neural dynamics involves the trade-off between tractability and biological realism. This is particularly important in exploring the range of possible dynamics of large networks. Our approach uses Gaussian white noise as a probe, thus capturing the full range of system responses and characteristics by using an approach inspired by the well-established Wiener - Volterra nonlinear system identification approach. We assess model behavior over a range of network architectures and noise stimulation rates and demonstrate non-monotonicity and nonlinearity as a system property. Perhaps surprisingly, our computational model suggests that recurrent systems of nonlinear neurons exhibit a range of complex behaviors that do not readily yield to linear modeling in every setting. Our results suggest that a linear interpretation of experimental data is likely to discount the critical importance of properties emerging from network architecture. The main contribution of this effort is to highlight the importance of the network's architecture operating on the nonlinear properties of individual neurons and the experimental probing approaches of the circuitry.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".