The impact of excitability heterogeneity and synaptic coupling on resilience and stability of a macro-scale brain network
Bibliographic record
Abstract
Experimental, and computational studies have highlighted the abundance and significance of excitability and synaptic heterogeneity for network resilience, learning, and memory. However, these studies have been confined to cellular-level investigations, a spatial resolution that is inaccessible with clinical tools (i.e., electroencephalography, magnetoencephalography). Such clinical recordings capture local field potentials, representing brain activity at a coarser spatial scale than individual neurons. To understand how neuronal diversity affects large-scale activity, computational models and techniques are needed to examine the effects of heterogeneity on dynamics at these coarser scales. We therefore examine how intrinsic excitability heterogeneity in neuronal populations of the brain affects the stability and resilience of macro-scale brain networks against external stimulations. We use a macro-scale computational model where each node is a neural mass model with interacting excitatory and inhibitory sub-populations. Heterogeneities are represented using lumped parameters, and brain region dynamics are coupled through a global synaptic coupling parameter. Our numerical results show that excitability heterogeneity and synaptic coupling stabilize neural dynamics against external inputs, reducing amplitude variations and enhancing resilience at the macro-scale. Excitability heterogeneity also prevents the emergence of multiple equilibria. Although the global coupling parameter alone is less effective at reducing the emergence of multiple equilibria, it boosts the network’s resilience when combined with heterogeneity. Thus, excitability heterogeneity stabilizes neural dynamics and simplifies the system’s stable states on a broader spatial scale.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".