Inhibition in oscillatory networks of neurons can have apparently paradoxical effects, sometimes creating dispersion of phases, sometimes fostering synchrony in the network. We analyze a pair of biophysically modeled neurons and show how the rates of onset and decay of inhibition interact with the timescales of the intrinsic oscillators to determine when stable synchrony is possible. We show that there are two different regimes in parameter space in which different combinations of the time constants and other parameters regulate whether the synchronous state is stable. We also discuss the construction and stability of nonsynchronous solutions, and the implications of the analysis for larger networks. The analysis uses geometric techniques of singular perturbation theory that allow one to combine estimates from slow flows and fast jumps.
All Science Journal Classification (ASJC) codes
- Statistical and Nonlinear Physics
- Mathematical Physics
- Condensed Matter Physics
- Applied Mathematics