Metaboplasticity: The Reciprocal Regulation of Neuronal Activity and Cellular Energetics

TL;DR

This study introduces a biophysically grounded model linking neuronal metabolism with activity and plasticity, revealing how energy states influence network dynamics, learning, and stability in spiking neural networks.

Contribution

It presents a novel conductance-based SNN model incorporating metabolic constraints via temperature-dependent scaling, uncovering emergent properties and stability mechanisms.

Findings

Metabolic states cause bifurcations in learning trajectories.

Thermal stress deforms STDP plasticity windows.

High energy states lead to network hypersynchronization.

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.