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Dynamics of adult neurogenesis in the hippocampus II
Collaborator: Susanne Lezius, Imke Kirste, Christoph Bandt, Gerd Kempermann

Based on the preceding project Dynamics of adult neurogenesis in the hippocampus, Part I and on new data from Imke Kirste in the group of Gerd Kempermann we have developed a refined model of adult neurogenesis, see Figure 1. It takes more cell types into account and has a better temporal resolution than the previous model.

Figure 1: Refined model of the dynamics of neurogenesis. Long boxes indicate cell populations that are in cell division; squares indicate cell populations that are not. In this model we differentiate all five cell types for which population data have been measured, only the first four can actually divide.

The model not only allows us to simulate the populations of labeled cells over time but also the background population of unlabeled cells and the labeling process. The simulation results fit the experimental data well and provide information also about parameters, such as division constants, bleaching time course, and cell death, that are difficult to get directly from the experiment.

Figure 2: Simulation results of the refined model in comparison to experimental data for the five different cell types measured over time after the labeling process. In the lower right panel is shown the background populations of cells as simulated without labels (in particular without bleaching) and as estimated experimentally. The model suggests that only a small fraction of Type 1 cells are active. The purple bar indicates the inactive Type 1 cells.

The model now serves as a basis for further experiments that can be designed more targeted than would be possible without such a model.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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