Neuron populations use variable combinations of short-term feedback mechanisms to stabilize firing rate

Abstract

Neurons tightly regulate firing rate and a failure to do so leads to multiple neurological disorders. Therefore, a fundamental question in neuroscience is how neurons produce reliable activity patterns for decades to generate behavior. Neurons have built-in feedback mechanisms that allow them to monitor their output and rapidly stabilize firing rate. Most work emphasizes the role of a dominant feedback system within a neuronal population for the control of moment-to-moment firing. In contrast, we find that respiratory motoneurons use 2 activity-dependent controllers in unique combinations across cells, dynamic activation of an Na+ pump subtype, and rapid potentiation of Kv7 channels. Both systems constrain firing rate by reducing excitability for up to a minute after a burst of action potentials but are recruited by different cellular signals associated with activity, increased intracellular Na+ (the Na+ pump), and membrane depolarization (Kv7 channels). Individual neurons do not simply contain equal amounts of each system. Rather, neurons under strong control of the Na+ pump are weakly regulated by Kv7 enhancement and vice versa along a continuum. Thus, each motoneuron maintains its characteristic firing rate through a unique combination of the Na+ pump and Kv7 channels, which are dynamically regulated by distinct feedback signals. These results reveal a new organizing strategy for stable circuit output involving multiple fast activity sensors scaled inversely across a neuronal population.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 23, 2023
Source ID
10.1371/journal.pbio.3001971

Entities

People

  • Joseph M Santin
  • Lara Amaral-silva
  • Min Hu
  • Sandy E. Saunders
  • Sarah Pellizzari

Organizations

  • National Institutes of Health
  • United States Department of Defense

Tags

Fields of Study

  • Biology

Readers

  • Cardiovascular Physiology
  • Control Systems Engineering.
  • Neuroscience