Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability

Abstract

The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of "leak" channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. Two proposed TLE seizure mechanisms were implemented in various degrees and combinations: recurrent GC excitation via mossy fiber sprouting and increased PP input. While increasing PP strength degraded pattern separation only gradually, already the slight elevation of sprouting drastically (non-linearly) impaired pattern separation. In most tested hyperexcitable networks, leaky GCs ameliorated pattern separation. However, in some sprouting situations with all-or-none seizure behavior, pattern separation was disabled with and without leaky GCs. In the mild sprouting (and PP increase) region of non-linear impairment, leaky GCs were particularly effective in restoring pattern separation performance. These results are compatible with the hypothesis that the experimentally observed intrinsic rescaling of GCs serves to maintain the physiological function of the DG network.

Keywords: computational model; electrophysiology; epilepsy; hippocampus; ion channel homeostasis.