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arxiv: 2507.15519 · v2 · pith:KHAJFSE5new · submitted 2025-07-21 · 🧬 q-bio.NC · math.DS

A Dynamical Blueprint for Brain State Organization

classification 🧬 q-bio.NC math.DS
keywords activityneuronalstatesbraindynamicaltransitionsbalanceblueprint
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The brain is not static: neuronal networks shift between contrasting modes of activity, alternating between active and quiescent regimes known as up and down states. Together with rhythmic oscillations, such modes of activity are fundamental to perception, memory, and information processing. However, the dynamical principles underlying the diverse repertoire of activity patterns and their transitions remain poorly understood. Here, we identify a geometric structure that governs dynamic states emergence and organizes neuronal networks transitions. We derive the conditions for its existence and demonstrate that it emerges robustly across canonical models of neuronal population dynamics. Near this organizing center, switches between oscillations, bistability and up and down states are orchestrated by the excitation-inhibition balance in the neuronal network. Thus, we show that excitation and inhibition do not simply modulate network activity but define the dynamical landscape from which distinct brain states emerge. We also consider neuron-astrocyte interactions and reveal how astrocytes can tune excitatory-inhibitory balance, therefore modulating the transitions between neuronal activity regimes. Overall, our results identify a general dynamical blueprint underlying the emergence, organization, and control of brain states.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Modeling sequential cognitive states via population level cortical dynamics

    math.DS 2026-05 unverdicted novelty 6.0

    A neural-network approximation of heteroclinic dynamics, interpretable as an Amari-type neural-field system, reproduces sequential transitions among cognitive states.