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To understand the fast computational mechanisms of the brain, one needs to be able to perform rapid measurements at several sites along a single neuron as well as to image large populations of neurons. Traditional two-dimensional measurements are severely limited for such kinds of endeavors since neurons are located in three dimensions. To overcome this problem, we have developed new solutions to perform three-dimensional functional imaging with large scanning ranges along the z direction. With our three-dimensional microscopes we are able to maintain point or trajectory scanning with a short pixel dwell time. The speed and scanning volume of our technique in combination with the ~ 800 µm penetration dept of two-photon technology makes our methodology very convenient for in vivo measurements of neuronal populations, too.
Katona et al. 2011 PNAS















Population activity has long been studied in the visual cortex. We conduct in vivo two-photon imaging of cell assemblies in the V1 area by using multicell bolus loading of a calcium indicator dye. Neuronal network responses are recorded during different visual stimulation (moving bar, moving grating or visual discrimination protocol). In addition, active cells are selected based on the previously recorded somatic activity and their dendritic responses are followed along with the network activity in three dimensions by using whole-cell patch clamp techniques. 
Katona et al. 2012 Nature Methods












Two-photon uncaging takes advantage of the high spatial and temporal resolution of two-photon excitation to study dendritic integration, a postsynaptic mechanism. Two-photon uncaging can also be used to map receptor densities (e.g. for GABA receptors) even in three dimensions. Used in combination with two-photon imaging, two-photon uncaging provides an opportunity to study the long-term structural and functional consequences of stimulation of structures such as dendritic spikes and dendritic spines. Besides performing experiments we develop new caging compounds and use these for our new measurements.
Katona et al. 2011 PNAS














It is thought that sharp wave-ripples (SPW-R) activity is involved in the process of memory consolidation. In order to occure spontaneous SPW-R activity we use such a developed recording chamber where the oxygenation is better. The properties of SPW-R events similar to what was found in vivo. We investigate spontaneous single cell (pyramidal cell and interneuron) neuronal activities during SPW-R in the hippocampus CA1 and CA3 region under in vitro conditions. Fast spiking (FS), PV+ basket cells (BCs) as the clockworks for neuronal oscillations are important elements of hippocampal neuronal networks. Thus, beside the pyramidal cells we focus on PV+ interneurons to reveal the dendritic calcium dynamics during SPW-R.To achieve this, we combine two-photon 2D and 3D microscopy, local field electrophysiology, single cell electrophysiology, and dendritic patch clamp recordings. To measure the pharmacological background of calcium dynamics in single cell dendrites, we use focal synaptic stimulation and two-photon uncaging of novel glutamate and GABA caged compounds. 
Chiovini B et al. 2010 Neurochem Res. 












Oscillatory network activity is a major determinant of memory consolidation in the hippocampus. We used whole hippocampal (in toto) preparation to examine these properties during spontaneous delta wave activity, since in this model most of the hippoccampal networks are stay intact. During these experiments we recorded the local field potential synchronously with the population activity from a selected cell assembly observed on the somatic level by two-photon imaging and multicell bolus loading of calcium indicator dye. This network model is also susceptible to measure dendritic integration in the single cell level, since by combining the network activity measurement with the recording of the subcellular responses from a chosen neuron which is whole-cell patch clamped and fill with a calcium indicator we are able to further understand how the multiple inputs from the networks define the output of that selected cell.