In: Research

December 18, 2019

Computational Modeling

The hippocampal circuits that store and recall spatial information are comprised of diverse cell types, each exhibiting distinct dynamics and complex patterns of synaptic connectivity. Thus, even highly specific experimental perturbations of a single component of these neuronal circuits can have counterintuitive effects on their internal dynamics and output. Computational modeling offers experimentalists a framework to integrate their knowledge, make explicit the assumptions of their conceptual models, and quantitatively predict how each element of a neuronal network is expected to respond to cell type- or projection-specific perturbations. In the Soltesz lab we build computational models in close collaboration with experimentalists, both in the lab, across Stanford, and at other institutions through a multi-site NIH BRAIN Initiative collaboration. Our large-scale network models of the hippocampus are continuously refined to incorporate newly obtained experimental constraints, and numerical simulations are carried out to test hypotheses, compare candidate biophysical and network mechanisms for memory storage and recall, and aide in the interpretation of physiological and behavioral experimental data. The ultimate goal of these efforts is to obtain a deep conceptual understanding of the cellular and network mechanisms that mediate “memory replay” events called sharp-wave ripples by simulating a large-scale model of the hippocampus that reproduces for the observed firing properties of all cell types during sharp-waves.

Lab Members

Research Engineer

Ivan Raikov

Postdoctoral Researcher

Prannath Moolchand

Graduate Student

Darian Hadjiabadi

Data Science Consultant

Ben Dichter

Postdoctoral Researcher

Alexandra Chatzikalymiou

Research Engineer

Ivan Raikov

I hold undergraduate and master’s degree in Computer Science from the Georgia Institute of Technology, and a PhD in Biomedical Sciences from the University of Antwerp. I am studying information processing in the hippocampus by means of highly detailed and realistic computational simulation of neuronal networks at 1:1 scale.  More broadly, I am interested in solving the enormous neuroinformatics challenges of computational neuroscience by developing sophisticated computational frameworks capable of expressing, organizing and managing the different types of data and algorithms associated with computational models of neural networks.

Postdoctoral Researcher

Prannath Moolchand

Prannath Moolchand is a postdoctoral researcher, having joined after pursuing a doctorate in Neuroscience as a Fulbright scholar at Brown University, where he also earned a Master’s degree from its prestigious Applied Math department.

He combines computational modeling with High Performance Computing techniques to build biophysically realistic models of hippocampal cells to understand cellular and network level dynamics during memory processes. An advocate of Theoretical Neuroscience, he is also interested in applying rigorous mathematical theorems from dynamics and stochastics to understand how channelopathies disrupt cellular electrophysiology and how the consequent neural miscommunication leads to diseased conditions, particularly epilepsy.

Graduate Student

Darian Hadjiabadi

Darian Hadjiabadi is a third-year Bioengineering Ph.D. student with backgrounds in Computer Science (B.S.) and Biomedical Engineering (B.S., M.S.) from Johns Hopkins University. He is modeling neural dynamics from epileptic zebrafish (whole brain, single-cell resolution) to understand how unreliable inhibitory control affects network stability. Using these “fish-specific” models, he wants to predict which neurons significantly destabilize networks and validate them with in-vivo experiments.

Data Science Consultant

Ben Dichter

Postdoctoral Researcher

Alexandra Chatzikalymiou

Alexandra Chatzikalymniou holds a bachelor’s and a master’s degree in Chemical Engineering from the University of Patras, Greece, and a PhD in Neuroscience and Physiology from the University of Toronto. In her PhD, Alexandra focused on the modelling of theta rhythms using both phenomenological and biophysical models of the rodent hippocampus. As part of her modelling work, she used and analysed state-of-the-art biologically detailed models of the rodent CA1 developed by the Soltesz lab, to understand elements of theta rhythm generation. Alexandra is interested in place cell formation during navigation, and ripple related mechanisms of memory recall and consolidation.