AI Insight
Researchers developed a mathematical model showing how nervous systems naturally develop heavy-tailed weight distributions in their connections through combined structural and synaptic plasticity driven by diffusion dynamics. The model demonstrates that while synaptic plasticity alone can create these distributions when neural activity remains local, adding adaptive rewiring allows heavy-tailed distributions to emerge even with widespread activity and generates convergent-divergent circuits common in biological nervous systems. The model successfully reproduces connectivity patterns observed in both C. elegans and mouse brain networks, suggesting these organizational principles are conserved across species of different complexity.
Why it matters
This work provides a unified framework for understanding how brains self-organize their connection patterns, which could inform the development of more efficient artificial neural networks and help explain fundamental principles of neural circuit formation. The findings may also aid in understanding neurological disorders involving abnormal connectivity patterns.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: The nervous system continuously adjusts connection strengths and reorganizes its structure to form and maintain complex connectivity patterns with heavy-tailed weight distributions. We propose a parsimonious model in which structural and synaptic plasticity are driven by common diffusion dynamics. Synaptic plasticity alone generates heavy-tailed weight distributions, but only when activity spreading remains predominantly local. However, when combined with structural plasticity through adaptive rewiring, the model also generates these distributions with more extensive activity flow. Furthermore, adaptive rewiring produces complex network structures with convergent-divergent circuits. These circuits contain motifs that are pervasive in nervous systems and are responsible for context-sensitive signal propagation and enhanced signal to noise ratios. Our model robustly reproduces these results across diverse dynamical regimes while capturing key connectivity features of both C. elegans and mouse brain networks. These findings suggest that the underlying principles are shared across species of varying complexity.