Intrinsic dynamics in neuronal networks. I. Theory

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Mammalian nervous system networks exhibit spontaneous activity in two main patterns: steady low-rate firing and rhythmic bursting.
• Understanding the mechanisms generating these firing patterns and the transitions between them is crucial for comprehending neural dynamics.

Purpose of the Study:

• To theoretically investigate how dynamic interactions between excitatory and inhibitory neurons generate distinct neural firing patterns.
• To determine the key parameters controlling transitions between steady firing and bursting in neuronal networks.

Main Methods:

• Utilized a semianalytic model based on mean firing rate dynamics.
• Employed simulations of large neuronal networks to analyze intrinsic dynamics.
• Examined the role of the fraction of endogenously active cells and network connectivity.

Main Results:

• The fraction of endogenously active cells is the primary determinant of firing patterns: increasing this fraction transitions networks from high firing rates to bursting, and then to low-rate steady firing.
• Network connectivity modulates whether activity is constant or oscillates around the mean firing rate.
• These findings rely on conventional assumptions of neuronal excitation, inhibition, and spike-frequency adaptation.

Conclusions:

• The proportion of intrinsically active neurons dictates spontaneous network activity patterns in the mammalian nervous system.
• Two experimentally testable predictions: low-rate firing networks require endogenously active cells, and reducing their fraction induces bursting.