MétaCan
Menu
Back to cohort
Record W7132992325

Adaptive myelination and its synchronous dynamics in the Kuramoto network model with state-dependent delays

2021· dissertation· W7132992325 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKuramoto modelControl theory (sociology)Stability (learning theory)Synchronization (alternating current)Limit (mathematics)Limit cycleTopology (electrical circuits)Multistability
DOInot available

Abstract

fetched live from OpenAlex

White matter pathways form a complex network of myelinated axons that play a critical role in brain function by facilitating the timely transmission of neural signals. Recent evidence reveals that white matter networks are adaptive and that myelin undergoes continuous reformation through behaviour and learning during both developmental stages and adulthood in the mammalian life cycle. Consequently, this allows axonal conduction delays to adjust in order to regulate the timing of neuron signals propagating between different brain regions. Despite its newly founded relevance, the network distribution of conduction delays have yet to be widely incorporated in computational models, as the delays are typically assumed to be either constant or ignored altogether. From its clear influence towards temporal dynamics, we are interested in how adaptive myelination affects oscillatory synchrony in the brain. We introduce a plasticity rule into the delays of a weakly coupled oscillator network, whose positions relative to its natural limit cycle oscillations is described through a coupled phase model. From this, the addition of slowly adaptive network delays can potentially lead coupled oscillators to a more phase synchronous limit cycle. To see how adaptive white matter remodelling can shape synchronous dynamics, we modify the canonical Kuramoto model by enabling all connections with phase-dependent delays that change over time. We directly compare the synchronous behaviours of the Kuramoto model equipped with static delays and adaptive delays by analyzing the synchronized equilibria and stability of the system's phases. Our mathematical analysis of the model with Dirac and exponentially distributed connection delays, supported by numerical simulations, demonstrates that larger, more widely varying distributions of delays generally impede synchronization in the Kuramoto network. Adaptive delays act as a stabilizing mechanism for the synchrony of the network by adjusting towards a more optimal distribution of delays. Adaptive delays also make global synchronization more resilient to perturbations and injury towards network architecture. Our results provide insights about the potential significance of activity-dependent myelination. We hope that these results lay out the groundwork to computationally study the influence of adaptative myelination towards large-scale brain synchrony.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.272
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it