Biophysical models of intrinsic homeostasis: Firing rates and beyond

TL;DR

This paper reviews biophysical neuron models that explain how intrinsic homeostasis mechanisms regulate firing rates and other cellular properties to maintain stable brain function amid changing conditions.

Contribution

It provides an overview of recent biophysical models of neuronal homeostasis and hypothesizes how cellular dynamics can be stably controlled.

Findings

Biophysical models identify physiological substrates regulating excitability.

Mathematical theory reveals computational properties from cellular dynamics.

Homeostatic mechanisms can stabilize firing rates and intrinsic properties.

Abstract

In view of ever-changing conditions both in the external world and in intrinsic brain states, maintaining the robustness of computations poses a challenge, adequate solutions to which we are only beginning to understand. At the level of cell-intrinsic properties, biophysical models of neurons permit one to identify relevant physiological substrates that can serve as regulators of neuronal excitability and to test how feedback loops can stabilize crucial variables such as long-term calcium levels and firing rates. Mathematical theory has also revealed a rich set of complementary computational properties arising from distinct cellular dynamics and even shaping processing at the network level. Here, we provide an overview over recently explored homeostatic mechanisms derived from biophysical models and hypothesize how multiple dynamical characteristics of cells, including their intrinsic neuronal excitability classes, can be stably controlled.