Data-driven, biologically constrained computational model of the hippocampal network at full scale

Ivan Soltesz, Stanford University

Usage Details

Information processing in the brain is organized and facilitated by the complex interactions of intrinsic biophysical properties of distinct neuronal types, neuronal morphology, and network connection topology. These properties give rise to specific types of network oscillations and other dynamic processes that govern neural information encoding and exchange. This project is designed to create a detailed picture at unprecedented scale of how the intrinsic properties of hippocampal principal neurons and interneurons define the network activity under normal conditions, and how pathological changes in those properties under epileptic conditions disrupt hippocampal function. The overarching goal of this activity is to construct a 1:1 scale, realistic and detailed computational model of the CA1-CA3-dentate gyrus network in the rat hippocampus and study physiological and pathophysiological network dynamics.

Additionally, the project aims to achieve broader impact in the neuroscience community by providing the software infrastructure necessary to put unprecedented petascale computing capability within reach of neuroscientists regardless of their expertise in high-performance computing. The support for GPUs and the Intel MIC architecture being developed in the widely-used NEURON simulator will enable neuroscientists to fully utilize supercomputing accelerator architectures and thus catalyze greater research productivity and pave the way for wider use of large-scale and full-scale detailed modeling of the brain.

Furthermore, the neuroscience model code and simulation support software being developed as part of the research goals behind this proposal will be made publicly available and will remove the barriers of setting up simulations and managing results data that traditionally have impeded the utilization of high-performance computing resources.