The Brain-Mesh Model: A Unified Framework For Neural Synchrony, Plasticity, And Coherence
| Autor: | Ballús, Andreu |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | The brain-mesh model introduces a novel three-layered architecture that integrates local and macro-regional connectivity with an underlying, mesh-inspired network layer. This foundational mesh layer, based on metallic mesh structures, spans the entire brain and generates interference patterns, noise, and resonance effects that modulate both local and global neural dynamics. The fused model goes beyond traditional connectivity frameworks by providing a unified explanation for phenomena such as brain-wide phase gradients, stable low-frequency resonance frequencies, and long-range plasticity effects, which are often difficult to explain cohesively within existing models. In addition to accounting for classical neurobiological observations, such as phase synchrony, functional connectivity fluctuations, and local Hebbian plasticity, the model offers novel insights into less understood phenomena. Specifically, it predicts connectivity-independent phase gradients across non-synaptic regions, harmonic resonance peaks consistent across individuals, and diffuse plasticity driven by global interference patterns, all of which are challenging to explain under current frameworks. These unique predictions align with partial empirical observations, such as traveling wave dynamics, consistent low-frequency oscillations, and task-induced connectivity shifts, underscoring the model's relevance. Additionally, the brain-mesh model generates testable hypotheses that distinguish it from traditional approaches. This provides a promising framework for future experimental validation and opens new avenues for understanding global brain function. Comment: This manuscript was prepared with assistance from AI tools for language refinement, drafting support, and formula verification. The preprint was originally posted on bioRxiv on 25 November 2024. Available at https://www.biorxiv.org/content/10.1101/2024.11.22.624908v1 |
| Databáze: | arXiv |
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