Abstract
The hippocampus is a widely studied brain region thought to play an important role in higher cognitive functions such as learning, memory, and navigation. The amount of data on this region increases every day and delineates a complex and fragmented picture, but an integrated understanding of hippocampal function remains elusive. Computational methods can help to move the research forward, and reconstructing a full-scale model of the hippocampus is a challenging yet feasible task that the research community should undertake.In this chapter, we present strategies for reconstructing a large-scale model of the hippocampus. Based on a previously published approach to reconstruct and simulate brain tissue, which is also explained in Chap. 10 , we discuss the characteristics of the hippocampus in the light of its special anatomical and physiological features, data availability, and existing large-scale hippocampus models. A large-scale model of the hippocampus is a compound model of several building blocks: ion channels, morphologies, single cell models, connections, synapses. We discuss each of those building blocks separately and discuss how to merge them back and simulate the resulting network model.
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