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Spatiotemporal Patterns of Granule Cell Activity Revealed by a Large-Scale, Biologically Realistic Model of the Hippocampal Dentate Gyrus

  • Gene J. YuEmail author
  • Phillip J. Hendrickson
  • Dong Song
  • Theodore W. Berger
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI)

Abstract

Interest in the hippocampus has generated vast amounts of experimental data describing hippocampal properties, including anatomical, morphological, biophysical, and synaptic transmission levels of analysis. However, this wealth of structural and functional detail has not guaranteed insight into higher levels of system operation.

In this chapter, we propose a computational framework that can integrate the available, quantitative information at various levels of organization to construct a three-dimensional, large-scale, biologically realistic, spiking neuronal network model with the goal of representing all major neurons and neuron types, and the synaptic connectivity, found in the rat hippocampus. In this approach, detailed neuron models are constructed using a multi-compartment approach.

Simulations were performed to investigate the role of network architecture on the spatiotemporal patterns of activity generated by the dentate gyrus. The results show that the topographical projection of axons between the entorhinal cortex and the dentate granule cells organizes the postsynaptic population into subgroups of neurons that exhibit correlated firing expressed as spatiotemporal clusters of firing. These clusters may represent a potential “intermediate” level of hippocampal function. Furthermore, the effects of inhibitory and excitatory circuits, and their interactions, on the population granule cell response were explored using dentate basket cells and hilar mossy cells.

Keywords

Spiking neuronal network model Large-scale Hippocampus Dentate gyrus Entorhinal cortex Compartmental model Anatomical connectivity Population dynamics Spatiotemporal clusters Inhibition Mossy cell associational pathway 

Notes

Acknowledgments

This work was supported by ONR Grant N00014-13-1-0211, NIBIB Grant P41 EB001978, and NIH Grant U01 GM104604. Computation for the work was supported by the University of Southern California Center for High-Performance Computing and Communications (www.usc.ed/hpcc).

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gene J. Yu
    • 1
    Email author
  • Phillip J. Hendrickson
    • 1
  • Dong Song
    • 1
  • Theodore W. Berger
    • 1
  1. 1.Department of Biomedical Engineering, Center for Neural Engineering, Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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