Samuel is a Royal Society Newton International Fellow and joined the lab in 2022 as a postdoc. In his research, he tries to understand how neural circuits develop and operate. For this he uses theoretical and computational methods to build models of biological neural networks that contain excitatory and inhibitory neurons. He obtained a Ph.D. at the Max Planck Institute for Brain Research (MPIBR) and the Frankfurt Institute for Advanced Studies (FIAS), where he demonstrated how recurrent networks with rich computational functions can emerge from realistic plasticity mechanisms. Currently, he is working on extending these models to explain memory processing in the hippocampus and homeostatic adaptation in sensory circuits.