A million-plus neuron model of the hippocampal dentate gyrus: Dependency of spatio-temporal network dynamics on topography

Abstract

This paper describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust, non-random pattern of spiking best described as spatiotemporal "clustering". To identify the network property or properties responsible for generating such firing "clusters", we progressively eliminated from the model key mechanisms such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatio-temporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as "functional units" that organize the processing of entorhinal signals.

Fig. 1

Simulation results for topographically constrained EC-DG networks with feedforward and feedback inhibition, run at two different scales: 1M granule cells (top), and 100k granule cells (bottom). At both scales, spatio-temporal clusters appear in the granule cell activity, despite the random nature of the EC input. In the million-cell case, only a subset of the full dataset is plotted to keep it from appearing solid black. Column B: 2D autocorrelations confirm the presence of these clusters.

Fig. 2

Granule cell activity when removing internal and external sources of inhibition. Top: GABAergic inhibition removed; bottom: both AHP and GABA removed. Spatio-temporal clusters persist in both cases, as evidenced by both the raster plots and 2D autocorrelations (B).

Fig. 3

Simulation results for a randomly connected EC-DG network. In this simulation, the granule cell AHP was removed, as was GABAergic inhibition. Spatio-temporal clusters are no longer present, having been replaced with bands of activity with a high level of background activity. In the 2D autocorrelation, what looked like a typical cluster is now a vertical band. Thus, while there’s still a temporal variation in granule cell activity, the spatial component is gone.