Resonant Interneurons Can Increase Robustness of Gamma Oscillations

Gamma oscillations are believed to play a critical role in in information processing, encoding, and retrieval. Inhibitory interneuronal network gamma (ING) oscillations may arise from a coupled oscillator mechanism in which individual neurons oscillate or from a population oscillator in which individual neurons fire sparsely and stochastically. All ING mechanisms, including the one proposed herein, rely on alternating waves of inhibition and windows of opportunity for spiking. The coupled oscillator model implemented with Wang-Buzsa ' ki model neurons is not sufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a tightly synchronized regime, the stochastic population oscillatormodelis often characterizedbysparse firing, whereasinterneuronsbothin vivo andin vitrodonot fire sparsely duringgamma, but ratheronaverage every other cycle. Wesubstituted so-called resonator neural models, which exhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness to heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic clampexperimentsthatshowautapse-inducedfiring in entorhinal cortical interneuronssupportthe idea thatPIRcanserve asanetworkgamma mechanism. Furthermore, parvalbumin-positive (PV+) cells were much more likely to display both PIR and autapse-induced firing than GAD2(+) cells, supporting the view that PV+ fast-firing basket cells are more likely to exhibit class 2 excitability than other types of inhibitory interneurons.