Low-Dimensional Maps Encoding Dynamics in Entorhinal Cortex and Hippocampus

Abstract

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.

The dynamic structure underlying subthreshold oscillatory activity and the onset of spikes in a model of medial entorhinal cortex stellate cells

“…The synchronization of SCs and networks including SCs have been studied by various authors (Acker et al, 2003;Netoff et al, 2004;Rotstein et al, 2005b;Pervouchine et al, 2005). We compared the synchronization properties of SCs and networks including SC using the reduced NAS and the full SC models (see Appendix B and Fig.…”

Using phase resetting to predict 1:1 and 2:2 locking in two neuron networks in which firing order is not always preserved

“…We have found a practical rule of thumb that prediction accuracy deteriorates when the network period is less than five times the synaptic time constant. This rule of thumb was borne out in a recent paper (Pervouchine et al 2006), which used very similar PRC methods on reduced two-neuron networks. They showed how the hippocampal CA1 oriens lacunosum-moleculare (O-LM) cells can only synchronize at theta rhythm frequencies in the presence of inhibitory interneurons with short time constants (5 ms), similar to the model interneurons used in this study.…”

The variance of phase-resetting curves

“…The accuracy of the analytical phase dependent variance will be compared to an ad-hoc phase dependent variance function we have used in a previous paper (Netoff et al 2005b). The data is pulled from the Netoff lab's experimental database containing phase response curves from hundreds of neurons collected for various experiments (Pervouchine et al 2006;Netoff et al 2005a, b). Details of the experimental protocols are published in those papers.…”