User Project Details

relearn-test

Test for Structural Plasticity at the Human-Scale

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Technische Universität Darmstadt

Fachbereich Informatik, Parallele Programmierung

Current imaging techniques cannot resolve the human brain to the full scale, nor can they show all the inner workings of the cells. To overcome this issue, computational neuroscience takes imaging data as input, uses their models (and computations) as predictions, and compares these results with imaging data from later. Structural plasticity describes how neurons change their connections freely, i.e., they can form new synapses and remove existing ones without restriction. We can observe these changes as part of processes such as learning or healing, especially after lesions. However, structural plasticity brings an intrinsic bottleneck: Calculating where a neuron connects (with a free choice) results in pairwise calculations and, thus, quadratic run time. Understanding processes and diseases—such as Alzheimer's—where the structural connectome changes require us to combine imaging techniques with models for structural plasticity. In this project, we want to lay the groundwork.