Biophysical models of intrinsic homeostasis: Firing rates and beyond.

Nelson Niemeyer, Jan-Hendrik Schleimer, Susanne Schreiber

Journal: Current opinion in neurobiology 2022;70():81-88

PMID: 34454303

Full Text on Elsevier Science View on PubMed

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.

Address: Institute for Theoretical Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany.; Institute for Theoretical Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany; Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany.; Institute for Theoretical Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany. Electronic address: [email protected].

DOI: 10.1016/j.conb.2021.07.011

Link outs

Subscription / membership required

Full Text Sources:

MeSH Terms: Action Potentials, Homeostasis, Models, Neurological, Neurons