Cells that produce intrinsic theta oscillations often contain the hyperpolarization-activated current I h . In this article, we use models and dynamic clamp experiments to investigate the synchronization properties of two such cells (stellate cells of the entorhinal cortex and O-LM cells of the hippocampus) in networks with fast-spiking (FS) interneurons. The model we use for stellate cells and O-LM cells is the same, but the stellate cells are excitatory and the O-LM cells are inhibitory, with inhibitory postsynaptic potential considerably longer than those from FS interneurons. We use spike time response curve methods (STRC), expanding that technique to three-cell networks and giving two different ways in which the analysis of the three-cell network reduces to that of a two-cell network. We show that adding FS cells to a network of stellate cells can desynchronize the stellate cells, while adding them to a network of O-LM cells can synchronize the O-LM cells. These synchronization and desynchronization properties critically depend on I h . The analysis of the deterministic system allows us to understand some effects of noise on the phase relationships in the stellate networks. The dynamic clamp experiments use biophysical stellate cells and in silico FS cells, with connections that mimic excitation or inhibition, the latter with decay times associated with FS cells or O-LM cells. The results obtained in the dynamic clamp experiments are in a good agreement with the analytical framework.

Gamma-frequency electroencephalogram oscillations may be important for cognitive processes such as feature binding. Gamma oscillations occur in hippocampus in vivo during the theta state, following physiological sharp waves, and after seizures, and they can be evoked in vitro by tetanic stimulation. In neocortex, gamma oscillations occur under conditions of sensory stimulation as well as during sleep. After tetanic or sensory stimulation, oscillations in regions separated by several millimeters or more occur at the same frequency, but with phase lags ranging from less than 1 ms to 10 ms, depending on the conditions of stimulation. We have constructed a distributed network model of pyramidal cells and interneurons, based on a variety of experiments, that accounts for near-zero phase lag synchrony of oscillations over long distances (with axon conduction delays totaling 16 ms or more). Here we show that this same model can also account for fixed positive phase lags between nearby cell groups coexisting with near-zero phase lags between separated cell groups, a phenomenon known to occur in visual cortex. The model achieves this because interneurons fire spike doublets and triplets that have average zero phase difference throughout the network; this provides a temporal framework on which pyramidal cell phase lags can be superimposed, the lag depending on how strongly the pyramidal cells are excited.