An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress
- 1Frankfurt Institute for Advanced Studies
Abstract
Neuronal computation is constrained by the energetic cost of maintaining synaptic transmission, ionic gradients and action-potential generation. Prior work has shown that food restriction in mice shifts visual cortical neurons toward a different operating regime characterized by reduced excitatory synaptic drive, increased membrane resistance and a more depolarized resting membrane potential. This preserves neuronal firing rates, but comes at the cost of reduced coding precision. Deriving an improved estimate of neuronal energy consumption, we have recently shown that these adaptations reflect an optimal trade-off between neuronal information transmission and energy consumption. However, it remains unclear through which control strategies individual neurons adapt their biophysical parameters to maintain a favorable energy-information trade-off. Here we consider several candidate models implementing different control strategies. A "synapses-first" model, in which metabolic stress drives a downscaling of synaptic efficacies to save energy, while an intrinsic plasticity mechanism increases excitability to preserve the average firing rate, successfully reproduces key experimental observations. Alternative models in which energy shortages drive changes in intrinsic excitability or firing-rate variability can also reproduce the endpoint of neuronal adaptation, but predict different time courses and exhibit less energy-efficient and less stable dynamics. New experiments are needed to distinguish among these competing models.
Related
- OPA1-dependent mitochondrial remodeling coordinates TCR signaling and metabolic adaptation during iNKT cell differentiation
- Smart scaffolds: how WD40 proteins integrate plant development, metabolism and stress adaptation
- Human Saliva Induces Acetoin-Dependent Metabolic Adaptation that Promotes Stress Tolerance in Streptococcus mutans
- Surface curvature promotes coordinated extracellular matrix remodeling and mechanotransductive signaling during osteoblast adaptation
- Coordinated Brain-Network Dynamics of Maternal Adaptation
BibTeX
@misc{sommer2026energydependent,
title = {{An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress}},
author = {P. Sommer and P. Jedlicka and A. D. Bird and J. Triesch},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.757140},
url = {https://www.biorxiv.org/content/10.64898/2026.10.28.757140v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress
AU - P. Sommer
AU - P. Jedlicka
AU - A. D. Bird
AU - J. Triesch
PY - 2026
DA - 2026/10/28
PB - bioRxiv
DO - 10.64898/2026.10.28.757140
UR - https://www.biorxiv.org/content/10.64898/2026.10.28.757140v1
AB - Neuronal computation is constrained by the energetic cost of maintaining synaptic transmission, ionic gradients and action-potential generation. Prior work has shown that food restriction in mice shifts visual cortical neurons toward a different operating regime characterized by reduced excitatory synaptic drive, increased membrane resistance and a more depolarized resting membrane potential. This preserves neuronal firing rates, but comes at the cost of reduced coding precision. Deriving an improved estimate of neuronal energy consumption, we have recently shown that these adaptations reflect an optimal trade-off between neuronal information transmission and energy consumption. However, it remains unclear through which control strategies individual neurons adapt their biophysical parameters to maintain a favorable energy-information trade-off. Here we consider several candidate models implementing different control strategies. A "synapses-first" model, in which metabolic stress drives a downscaling of synaptic efficacies to save energy, while an intrinsic plasticity mechanism increases excitability to preserve the average firing rate, successfully reproduces key experimental observations. Alternative models in which energy shortages drive changes in intrinsic excitability or firing-rate variability can also reproduce the endpoint of neuronal adaptation, but predict different time courses and exhibit less energy-efficient and less stable dynamics. New experiments are needed to distinguish among these competing models.
ER -