Characterization and quantification of synaptic populations

Plasticity of synapses is an integral component during development and aging, is further assumed to form the basis of learning and memory and is implicated in a range of diseases. Synapses are not believed to exert their influence as individuals. Instead, the concerted interplay of multiple synaptic populations are assumed to take place during behaviour.

A range of mechanisms have been proposed to participate in the regulation of synaptic populations, including homeostatic scaling and excitation-inhibition balance. This project proposes to develop and apply methods to quantitatively describe the population.

Our computational pipeline produces estimates of the shape of the distribution in terms of particular distribution types or statistical moments and in particular produces estimates of the upper tail corresponding to the strongest synapses. We are further able to detect where in the brain changes occurr and how changes influence the hierarchical taxonomy.

We apply the methods to data from fluorescence microscopy, 3D-electron microscopy and in vivo STED super-resolution microscopy where thousands to millions of individual synapses are studied. In particular, we are able to detect and describe the changes to different sub-populations brain-wide.

We investigate these changes as a consequence of experimental conditions including development and aging and sleep-wake as well as changes due to environmental enrichment and disease-associated genetic knock-out perturbations.