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

Abstract

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.

The Effects of Bipolar Disorder Granule Cell Hyperexcitability and Lithium Therapy on Pattern Separation in a Computational Model of the Dentate Gyrus

“…One area of promise for identifying predictors may be behavioural tasks that are hypothesised to probe lower-level DG neural computational functioning, such as MD. In a previous study, we hypothesised that PS may underlie MD performance [44], based on two lines of evidence for the DG’s involvement 1) during MD [37, 65], and 2) in PS [33, 34]. Interestingly, a pilot study of the effects of lithium therapy in BD on MD performance demonstrated that lithium therapy significantly improved MD performance in LRs only [39].…”

“…N indicates the number of cells per population. This circuit diagram was adapted from our previous paper [44]. D) The pattern separation (PS) performance of these networks were then assessed, by presenting the network with a series of partially overlapping PP input patterns, and assessing whether the resulting output patterns were less correlated.…”

“…We adopted a previously established spatiotemporal PS task to assess our network’s PS functioning [42, 44, 47]. This protocol involved simulating 24 partially overlapping patterns, varying smoothly in degrees of overlap, of PP inputs over a 200 ms window.…”

“…We then adapted an established computational model of the hippocampal dentate GC [41][42][43][44] implemented in the NEU-RON simulation environment (v. 8.0) [45]. This model has two identical dendrites with four compartments each, and a single compartment for the soma (Figure 1B).…”

“…PP cells were modelled as point processes that stimulated GCs and BCs. All biophysical properties were kept the same as in the original model [42][43][44]. Parameter values for the connectivity, cellular biophysics, and synaptic double-exponential functions can also be found in our Supplementary Materials, and are also described in our previous study that employed this model [44].…”