Toggling between gamma-frequency activity and suppression of cell assemblies.
Walker, Bryan L.
- Gamma (30-80 Hz) rhythms in hippocampus and neocortex resulting from the interaction of excitatory and inhibitory cells (E- and I-cells), called Pyramidal-Interneuronal Network Gamma (PING), require that the I-cells respond to the E-cells, but don't fire on their own. In idealized models, there is a sharp boundary between a parameter regime where the I-cells have weak-enough drive for PING, and on... read moree where they have so much drive that they fire without being prompted by the E-cells. In the latter regime, they often de-synchronize and suppress the E-cells; the boundary was therefore called the "suppression boundary" by Börgers and Kopell (2005) The model I-cells used in the earlier work by Börgers and Kopell have a "type 1" phase response, i.e., excitatory input always advances them. However, fast-spiking inhibitory basket cells often have a "type 2" phase response: Excitatory input arriving soon after they fire delays them. We study the effect of the phase response type on the suppression transition, under the additional assumption that the I-cells are kept synchronous by gap junctions. When many E-cells participate on a given cycle, the resulting excitation advances the I-cells on the next cycle if their phase response is of type 1, and this can result in suppression of more E-cells on the next cycle. Therefore strong E-cell spike volleys tend to be followed by weaker ones, and vice versa. This often results in erratic fluctuations in the strengths of the E-cell spike volleys. When the phase response of the I-cells is of type 2, the opposite happens: Strong E-cell spike volleys delay the inhibition on the next cycle, therefore tend to be followed by yet stronger ones. The strengths of the E-cell spike volleys don't oscillate, and there is a nearly abrupt transition from PING to ING (a rhythm involving I-cells only)read less
- C. Börgers and B. Walker, "Toggling between gamma-frequency activity and suppression of cell assemblies," Frontiers in Computational Neuroscience, vol. 7, 2013. doi: 10.3389/fncom.2013.00033.