Our research focus lies in identifying relationships between neuronal signals at multiple spatial and temporal scales, in particular neuronal spiking activity and population signals such as the local field potential. We aim to understand how neuronal assemblies, i.e., groups of neurons characterized by coordinated spiking, are related to population oscillations, a signature of synchronized network dynamics. Such knowledge will help to connect behavior-related changes in patterns of functional connectivity experimentally observed for different measurement modalities, offering a promising approach to better understanding the principles of brain processing.
New challenges for the analysis of electrophysiological data sets arise from the complexity of modern, massively parallel data in the presence of complicated experimental paradigms. To deal with these increased demands, we design and improve on workflows for a coherent, collaborative, and reproducible analysis workflow and develop corresponding software tools in close collaboration with researchers from the field of Neuroinformatics.
Torre E, Picado-Muiño D, Denker M, Borgelt C, Grün S (2013). Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining. Front Comput Neurosci 7:132.
Denker M, Roux S, Linden H, Diesmann M, Riehle A, Grün S (2011). The local field potential reflects surplus spike synchrony. Cereb Cortex 21:2681-2695.
Denker M, Riehle A, Diesmann M, Grün S (2010). Estimating the contribution of assembly activity to cortical dynamics from spike and population measures. J Comp Neurosci 29:599-613.
Sharott A, Moll C, Engler G, Denker M, Grün S, Engel A (2009). Different subtypes of striatal neurons are selectively modulated by cortical oscillations. J Neurosci 29:4571-4585.
Denker M, Timme M, Diesmann M, Wolf F, Geisel T (2004). Breaking synchrony by heterogeneity in complex networks. Phys Rev Lett 92: 074103.