An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress

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.