Abstract:The brain, as the organ of perception, memory, emotion and action, as well as the most complex thing in the (known) universe, exerts a compelling fascination to researchers. According to many scientists, we are at the beginning of a new era in neuroscience that is characterized by particularly rapid knowledge advancement, mainly due to new and powerful experimental techniques. Possibly this will lead to an understanding of the fundamental principles of brain function. All four scientific avenues are utilized in neuroscience: experimental, phenomenological-descriptive, the- oretical and computational approaches. Corresponding to the complexity of its object of study, the aca- demic field comprises about 20 different branches, with anatomical, physiological and cognitive sciences as major categories. The branches can also be sorted in sciences dealing with (1) molecular and cellular objects, (2) neural circuits and neural systems, (3) cognitive and behavioral aspects, and (4) translational and medical aspects. In relation to visualization (here used in place of data visualization, knowledge visualization, visual analysis, visual analytics,...) neuroscience plays two roles: on one side, it represents an application domain dealing with a huge amount of highly complex data that must be visualized and analyzed in order to distill insights. On the other side, it is a supportive science providing answers to perceptual and cognitive questions in visualization. In the talk, both relations will be examined, highlighting a few of the many exciting perspectives. Considering neuroscience as an application domain, the abundant opportunities to support data explo- ration, data filtering, data analysis, hypothesis-generation, and modeling by interactive visual tools are obvious. Such tools are necessary on all spatiotemporal scales: from molecular, to sub-cellular, cellu- lar, microcircuit and macrocircuit levels with their different time scales. In the talk I will discuss some tools, focusing on the tasks that are related to revealing the layout of anatomical neural connections (the connectome) and the configuring of functional networks by self-organizing processes. Specific top- ics addressed will be the dense reconstruction of neuronal circuits using electron tomography and the high-speed mapping of brain circuitry using optogenetic technologies. Beside the traditional approach of single-hypothesis testing, also hypothesis generation and prioritization will become important, as omics- or discovery-based methods are emerging in neurosciences. One of the most exciting and challenging frontiers in neuroscience thus involves harnessing the value of large-scale genetic, genomic, phenotypic, connectomics and physiological data sets, as well as the development of tools for data integration and mining. New methods for analysis of dynamical networks will offer the promise of integrating these different types of data and thereby will provide a more integrative under- standing. Now considering the second role of neuroscience as a supportive science - imagining that a deeper un- derstanding of brain functions has been achieved and computational models have been established to simulate aspects of human attention, perception and cognition, as well as refined tools in cognitive neu- roscience have been developed, to conduct sophisticated experiments. This will foster great benefits for our field, as for instance fundamental questions of HCI and visualization potentially can be answered. Among these questions are, e.g.: How do humans perceive, interpret and use visual information? How effective are visual representations? How do visual means facilitate cognitive tasks? How do humans interact with graphical representations in order to make sense of a situation? How do forms of interaction contribute to understanding and familiarity? How do changes happen in understanding, how do insights emerge? How to design effective graphical representations? How to design interactive visual systems? How to evaluate graphical representations? How to evaluate interactive visual systems? More detailed answers to these old questions might arise only in the distant future. In the talk it will be explained how modern methods of cognitive psychology can be employed already today, e.g., to evaluate more closely the effectiveness of visual representations. All in all, in the foreseeable future, neuroscience and visualization will move towards each other - with great and mutual benefit.