Supercomputing the Hippocampal Neuronal Machine

Ivan Soltesz

(Stanford University)

Please LOG IN to view the video.

Date: November 28, 2016


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 connectivity. These properties give rise to specific types of behaviorally relevant network oscillations and other dynamic processes that govern neural information encoding and exchange. We constructed a strictly data-driven, supercomputer-based, full-scale (1:1) computational model of the CA1 region of the hippocampus in order to increase our understanding of how the intrinsic properties and synaptic connectivity of hippocampal principal neurons and interneurons give rise to rhythmic network activity. Simulations of the full-scale CA1 model revealed that theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged even without rhythmic inputs. Furthermore, perturbations of the network connectivity pointed to sharply different roles for interneuronal types, and highlighted interneuronal diversity and GABAB receptors as key factors in theta rhythm generation. We also developed a novel modeling method called Network Clamp that allows the rational derivation of simpler models that can be run on personal computers from complex full-scale models. Supported by the BRAIN Initiative, NINDS, NASA, NSF and the Stanford Neuroscience Program.

Created: Wednesday, November 30th, 2016