To better understand the computational mechanism of the brain advanced techniques are necessary to measure from a large population of neurons in great detail and subcellular resolution. The main focus of our group is the development and application of novel imaging techniques accompanied by advanced software and hardware technologies. Our recent innovative developments enabled us to track the activity of the entire dendritic tree of a neuron in three dimensions and to record hundreds of cells simultaneously in an intact neuronal network (Katona et al. 2011 PNAS, Katona et al. 2012 Nature Methods, Szalay et al. 2016). Using these technologies, we highlighted the importance of neuronal signal processing at the neuronal spine level during hippocampal rhythms (Judak et al. 2022 Nature Comm.) and showed how positive and negative reinforcement affects the sensory processing of local neural networks, providing further details for a network-level model of contextual learning (Szadai et al. 2023 ELife).
Our key research, utilizing parallel behavioral tests and cell network measurements, could add an impactful contribution to the development of brain-computer interfaces. Not only we can study cells involved in perception, but also demonstrate that brain networks can learn to react to external stimuli. The same technique enables us to map malfunctioning cells in epilepsy models, which can potentially contribute to a novel type of epilepsy therapy.