Predicting Anatomically Realistic Cortical Connectomes using Statistical Inference
The framework’s usability will be tested by comparing its predictions of connectivity in layer 5 of rat primary somatosensory cortex against novel in vivo-based connectivity measurements. Preliminary results let us expect that the envisioned approach has potential to reveal local rules that underlie global synaptic organization in neocortical circuits. If this applies, the project will provide a foundation for studying the relationships between structural properties of neuronal networks, their underlying principles of synaptic organization and cortical functions. In addition, the framework will serve to investigate developmental mechanisms leading to these relationships and to analyze the structural origin or correlates of cortical malfunctions during pathological conditions.
We will combine complementary expertise in data science, Bayesian statistics and in vivo-based neuroanatomy to build, test and utilize the framework.
Professor Hans-Christian Hege
Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB)
Department Visual Data Analysis
Dr. Jakob Macke
Dr. Marcel Oberlaender