Towards a large-scale biologically realistic model of the hippocampus

Abstract

Real neurobiological systems in the mammalian brain have a complicated and detailed structure, being composed of 1) large numbers of neurons with intricate, branching morphologies--complex morphology brings with it complex passive membrane properties; 2) active membrane properties--nonlinear sodium, potassium, calcium, etc. conductances; 3) non-uniform distributions throughout the dendritic and somal membrane surface of these non-linear conductances; 4) non-uniform and topographic connectivity between pre- and post-synaptic neurons; and 5) activity-dependent changes in synaptic function. One of the essential, and as yet unanswered questions in neuroscience is the role of these fundamental structural and functional features in determining "neural processing" properties of a given brain system. To help answer that question, we're creating a large-scale biologically realistic model of the intrinsic pathway of the hippocampus, which consists of the projection from layer II entorhinal cortex (EC) to dentate gyrus (DG), EC to CA3, DG to CA3, and CA3 to CA1. We describe the computational hardware and software tools the model runs on, and demonstrate its viability as a modeling platform with an EC-to-DG model.

Figure 1

4,040-CPU Beowulf cluster used to run simulations. NEURON running over MPI was used to run the simulations, with Python being used to specify the model and perform data visualization/analysis.

Figure 2

Sample of 8 granule cell morphologies generated using L-Neuron.

Figure 3

Simulation results from model with 100,000 unique dentate granule cells, 6,600 medial entorhinal cortical (MEA) cells, 4,600 lateral entorhinal cortical (LEA) cells, and 1,000 basket cells. Topology/connectivity was biologically based, with sub-regions of the MEA & LEA projecting to sub-regions of the dentate gyrus. Spike timing was variable. The spatio-temporal clustering of spikes in the 2nd half of the simulation is noteworthy, considering the uniform nature of the input.