Granule cells perform frequency-dependent pattern separation in a computational model of the dentate gyrus

Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a U-shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesize that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative U-shaped PS relationship. We implemented a biophysical model of the DG that produces the hypothesized U-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network's PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the U-shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms. Lesioning synapses onto GCs did not impact the U-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a U-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs. We conclude that independent of network-level inhibition, GCs may intrinsically be capable of producing a U-shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganization and high frequency filtering.

Automated summary

This study investigates the mechanisms underlying frequency-dependent mnemonic discrimination in the hippocampal dentate gyrus using a computational model. The findings suggest that both cellular and network-level mechanisms contribute to the performance of mnemonic discrimination, and there is a U-shaped relationship between input frequency and pattern separation.