Topic: posterior distribution

The generation of appropriate parameter configurations for high-dimensional hierarchical spiking neural network models is currently one of the slowest and most frustrating tasks in the computational neuroscience workflow. Moreover, many of the currently used methods for multidimensional parametrization only provide single-point estimates, which do not allow deeper insights into the parameter regions of interest and parameter interactions. Having better knowledge about this would allow neuroscientists to understand more about the network dynamics and the relationships between structure and func