Homeostatic Adaptation of Optimal Population Codes under Metabolic Stress

2025-07-10

AI Summary

Neural populations face a fundamental trade-off among maintaining firing-rate homeostasis, adhering to metabolic energy constraints, and coping with increased coding noise under metabolic stress—a challenge inadequately captured by existing models. To address this, we propose a novel energy-budget model that explicitly couples ATP consumption to neural coding noise for the first time. By integrating firing-rate homeostasis approximations and hard energy constraints, we formulate an energy-dependent decentralized Poisson noise framework. Combining biophysically grounded simulations with optimal path theory, we derive energy-optimized coding strategies across varying ATP availability. Our model successfully reproduces key experimental observations in mouse visual cortex under chronic caloric restriction: sustained firing-rate homeostasis alongside broadened (flattened) tuning curves. This work establishes an interpretable mathematical foundation for low-power neural coding and extends classical efficient coding theory by embedding biophysical energy constraints directly into the coding optimization principle.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Information processing in neural populations is inherently constrained by metabolic resource limits and noise properties, with dynamics that are not accurately described by existing mathematical models. Recent data, for example, shows that neurons in mouse visual cortex go into a "low power mode" in which they maintain firing rate homeostasis while expending less energy. This adaptation leads to increased neuronal noise and tuning curve flattening in response to metabolic stress. We have developed a theoretical population coding framework that captures this behavior using two novel, surprisingly simple constraints: an approximation of firing rate homeostasis and an energy limit tied to noise levels via biophysical simulation. A key feature of our contribution is an energy budget model directly connecting adenosine triphosphate (ATP) use in cells to a fully explainable mathematical framework that generalizes existing optimal population codes. Specifically, our simulation provides an energy-dependent dispersed Poisson noise model, based on the assumption that the cell will follow an optimal decay path to produce the least-noisy spike rate that is possible at a given cellular energy budget. Each state along this optimal path is associated with properties (resting potential and leak conductance) which can be measured in electrophysiology experiments and have been shown to change under prolonged caloric deprivation. We analytically derive the optimal coding strategy for neurons under varying energy budgets and coding goals, and show how our method uniquely captures how populations of tuning curves adapt while maintaining homeostasis, as has been observed empirically.

Problem

• Model neural population coding under metabolic constraints
• Explain firing rate homeostasis during energy stress
• Link ATP use to noise levels in neurons

Innovation

• Energy budget model linking ATP use to coding
• Dispersed Poisson noise model for energy constraints
• Optimal decay path for least-noisy spike rates