# Full-scale biophysical modeling of hippocampal networks during spatial navigation and memory replay

> **NIH NIH U19** · STANFORD UNIVERSITY · 2020 · $466,251

## Abstract

Throughout our investigation of the mechanisms underlying hippocampal memory replay, we will
employ a strategy of continuous interaction between experimental and modeling approaches. First,
we consider conceptual models, and formulate hypotheses relevant to the initiation, generation and
termination of SPW-Rs. Then, as we gather critical new experimental data to test those hypotheses in
Projects 1-4, we will use that data to constrain full-scale biophysically-detailed computational models
of hippocampal networks, integrating a newly developed model of CA3 with existing models of dentate
gyrus (DG) and CA1. These models will then be used to predict the complex responses of the network
to simulated perturbations, help interpret experimental outcomes, inform hypotheses about underlying
principles, and suggest new experiments to test those hypotheses. In addition to an improved
conceptual understanding of memory replay, our final product will include full-scale network models of
the hippocampus with unprecedented functionality, fully open-sourced and freely available to the
broader neuroscience community.
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## Key facts

- **NIH application ID:** 9993616
- **Project number:** 5U19NS104590-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** IVAN SOLTESZ
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $466,251
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9993616

## Citation

> US National Institutes of Health, RePORTER application 9993616, Full-scale biophysical modeling of hippocampal networks during spatial navigation and memory replay (5U19NS104590-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9993616. Licensed CC0.

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