My name is Daniel Müller-Komorowska and I am a neuroscience PhD student. I combine patch clamp electrophysiology, optogenetics and neuronal network simulations to study the neuronal circuits involved in memory. Pattern separation is a neuronal computation that is thought to be crucial for both memory formation and retrieval. It allows us to remember and recall two very similar scenarios separately, keeping memories apart.

One brain area known to perform pattern separation is the dentate gyrus. During my PhD I ported a biophysically realistic network model of the dentate gyrus to Python and adapted it to incorporate electrophysiological data from me and my colleagues. With this model we were able to predict that pattern separation works better when the input slow gamma (30Hz) modulated as opposed to theta (10Hz) modulated.

Another project of mine has been about genetically targeting somatostatin positive interneurons in area CA3 of the hippocampus. Those cells are primarily involved in feedback inhibition. We found that a trangenic mouse line thought to target somatostatin interneurons with the Cre-Lox system, also targets CA3 pyramidal cells. Therefore, Cre dependent optogenetic stimulation does not only evoke inhibition, as would be expected from interneurons, it also cause strong excitation through the pyramidal cells.


Müller-Komorowska, D., Opitz, T., Elzoheiry, S., Schweizer, M., Ambrad Giovannetti, E., & Beck, H. (2020). Nonspecific Expression in Limited Excitatory Cell Populations in Interneuron-Targeting Cre-driver Lines Can Have Large Functional Effects. Frontiers in Neural Circuits, 14(April), 1–13. https://doi.org/10.3389/fncir.2020.00016

Braganza, O., Müller-Komorowska, D., Kelly, T., & Beck, H. (2020). Quantitative properties of a feedback circuit predict frequency-dependent pattern separation. ELife, 813188. https://doi.org/10.7554/eLife.53148