{"ID":2825231,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21659","arxiv_id":"2512.21659","title":"Metaboplasticity: The Reciprocal Regulation of Neuronal Activity and Cellular Energetics","abstract":"Standard Spiking Neural Network (SNN) models typically neglect metabolic constraints, treating neurons as energetically unconstrained components. We bridge this gap by implementing a conductance-based leaky integrate-and-fire (gLIF) microcircuit (N=5,000) in Brian2, using temperature-dependent Q10 scaling to as a biophysically grounded proxy to couple metabolic state with intrinsic excitability and synaptic plasticity. Our simulations revealed five distinct emergent properties: (1) Dynamics Bifurcation: Learning trajectories diverged significantly, with hypometabolic states plateauing near baseline and hypermetabolic states exhibiting non-linear, runaway potentiation; (2) STDP Window Deformation: Thermal stress structurally deformed the plasticity kernel, where hypermetabolism sharpened coincidence detection and hypometabolism flattened synaptic integration; (3) Signal Degradation: While metabolic rate positively correlated with connectivity strength, high-energy states caused synaptic saturation and a loss of sparse coding specificity; (4) Topological Shift: Network activity transitioned from sparse, asynchronous firing in energy-restricted states to pathological, seizure-like hypersynchronization in high-energy states ; and (5) Parametric Robustness: Sensitivity analysis confirmed these attractor states were intrinsic biophysical properties, robust across random network initializations. Collectively, these results define an \"inverted-U\" relationship between bioenergetics and learning, demonstrating that metabolic constraints are necessary hardware regulators for network stability.","short_abstract":"Standard Spiking Neural Network (SNN) models typically neglect metabolic constraints, treating neurons as energetically unconstrained components. We bridge this gap by implementing a conductance-based leaky integrate-and-fire (gLIF) microcircuit (N=5,000) in Brian2, using temperature-dependent Q10 scaling to as a bioph...","url_abs":"https://arxiv.org/abs/2512.21659","url_pdf":"https://arxiv.org/pdf/2512.21659v1","authors":"[\"Ece Öner\",\"Cenk Denktaş\"]","published":"2025-12-25T12:57:50Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[]","has_code":false}