## 4 Discussion  
The two use cases presented in this article are just two examples that illustrate different tasks for the exploratory analysis of neuronal data. The first one deals with the navigation across a visually complex set of neurons, where complexity comes both from the number of cells that compose it (in the order of thousands) and from the intricate anatomy of the neurons themselves. The visualization of a neuronal forest composed of hundreds or thousands of cells yields cluttered, visually complex images where it is difficult to distinguish one element from another. In this context, the use of abstract representations that simplify neuron anatomy decreases drastically visual clutter, displaying the most relevant information while eliminating minor details. Additionally, structuring these schematic representations at different levels of abstraction significantly simplifies the  
Figure 8: Spine comparison results from a human pyramidal neuron. (**a**) Maximum diameter and minimum mean neck diameter; (**b**) maximum mean neck diameter; (**c**) maximum membrane potential peak. Figure 7: The PyramidalExplorer tool. amount of information associated with the different organizational levels of the brain, taking therefore advantage of the benefits of encapsulation when analyzing complex systems. Overall, the combination of schematic representations with the exploitation of the levels of abstraction concept makes it possible to reduce both visual and cognitive complexity when visually exploring complex neural scenes. However, the visualization of the real neuron anatomy is still necessary for studying their morphological features. For that reason, the approach presented here combines schematic representation at different levels of abstraction with the visualization of morphologically correct representations. This generates clean and compact views that are efficiently rendered at rates suitable for interactivity. Additionally, this approach provides combinations of schematic representations that provide overall views with more detailed data, obtained at lower levels of abstraction, when and where needed. For example, in Figure 4, it is easy to see that layer 6 in the column contains far more pyramidal neurons than interneurons. Since the abstraction levels are connected, a simple click on the red bar representing the number of interneurons would allow to display these cells instantly, descending to the minicolumn level or the cell level as desired. Furthermore, since the abstract view and the geometrically realistic view are also connected, once a particular object has been spotted in the abstract representation, clicking on it can automatically highlight it in the realistic view and zoom in for an easy location (Figure 4b). Being able to go from abstract to realistic views provides many additional advantages, mainly because it lets users to select the kind of representation better suited to a particular task. For example, abstract views facilitate arranging data according to different criteria, since in these representations the objects spatial positions do not necessarily have to correspond to their real anatomical positions. This way, objects in the abstract image can be reordered according to size, volume, or any meaningful feature, facilitating filtering and ranking operations. After some elements are selected, it is always possible to display them in their original locations. The proposed framework facilitates creating more powerful tools. For example, use cases 1 and 2 can be connected, so that the exploration of a region of the neocortex can provide a selection of pyramidal neurons from which the dendritic spines will be later on analyzed. In general, once certain particular objects have been spotted through the interaction with the tools, as in the first use case, the framework allows to go beyond and focus on one of them, descending to a lower level and combining information from different nature, as it happens in the second use case with functional and morphological information.