NAP-B Pattern Research Group
Department
Theoretical Physics

Group leader: András Telcs

The website of the group: pattern.wigner.mta.hu

Our nervous system represents information in the activity patterns of the neurons, and the transformation of these patterns constitutes the basis of processing. Due to improving computing capacity, the activity of hundreds of cells can now be measured, with a good time-resolution. As a result, we should be able to understand a higher level of interactions between both neurons and areas of the brain, and also the rules of the transformation of the patters, i.e., the syntax of neuronal language. However, the methods allowing us to analyse such a high-dimensional mass of data are still lacking. In addition to executing complex experiments, planning these, setting up relevant theoretical models (frames) and analysing high-dimensional data all require a variety of expertise and an interdisciplinary approach. The groups of the MTA Wigner RC and the MTA Institute of Experimental Medicine (KOKI) collaborate to develop new analysis methods, on the basis of previous research on hippocampal activity patterns. These methods are going to be developed combining statistical, machine learning and theoretical neuroscience tools and be sensitive to the structure of cortical patterns and to the analysis of the rules governing their interactions. We are going to use optogenetic methods and imaging procedures to measure the activity of large groups of hippocampal stimulating cells in vitro an in vivo, in states of different dynamics. We are going to complement the measurements of KOKI with multi-electrode cell activity measurements from public databases. We wish to understand what neural patters are behind memory function, what happens when a new memory trace is formed or activated in the network. How does the inner dynamics of the network interact with external output? How are representations formed? How stable are the patterns in case of perturbations? Interdisciplinary collaboration enables us to use the following, extremely efficient learning cycle: measurement > theory > model > prediction > measurement. Data analysis, which will require an important computing capacity, will be supported by the Wigner RC Data Center. In addition to our results, we also wish to publish the methods we have developed, so that they can be used by other research groups in the analysis of their own measurement data.