Patterns of synchronized brain activity have been widely observed in EEGs and multi-electrode recordings, and much study has been devoted to understanding their role in brain function. We introduce the problem of visualization of synchronized behavior and propose visualization techniques for assessing temporal and spatial patterns of synchronization from data. We discuss spike rate plots, activity succession diagrams, space-time activity band visualization, and low-dimensional projections as methods for identifying synchronized behavior in populations of neurons and for detecting the possibly short-lived neuronal assemblies that produced them. We use wavelets conjunction with these visualization techniques to extract the frequency and temporal localization of synchronized behavior. Most of these techniques can be streamed, making them suitable for analyzing long-running experimental recordings as well as the output of simulation models.
The relative values of a single 2D scalar field can be effectively perceived when the field is represented by height and rendered as a surface in three dimensions. When multiple scalar fields are simultaneously rendered on the same domain, control of additional rendering attributes such as color, transparency, texturing, and lighting is needed for effective perception. However, the transition from foreground to background due to surface intersections can confound accurate surface perception, even for techniques that work well for overlapping non-intersecting surfaces. We propose a general method for decomposing and stratifying multiple intersecting surfaces into layered components to permit complete rendering control of each surface component. We explore different rendering schemes based on stratification values and report initial results from experimentation.
Davis (Data Viewing System) is a general-purpose data viewer designed for the simultaneous display of a large number
of dynamic data sets. Davis was inspired by the need to explore computational models of the cerebral cortex. These
systems are distinguished by complex dynamic elements interconnected in irregular patterns. Neuroscientists study the
detailed behavior of individual elements and how these elements interact to achieve cortical function. This paper
describes Davis and its use in cortical visualization.
Davis is written in Java and can be run from a browser or as a standalone application. Users must provide an XML
description of their data, which Davis uses for its menus, browsing and visualization. Davis visualizations can be
applied to any collection of space-time data sets, and the Davis infrastructure allows visualizations to be added easily.
Davis handles the synchronization of different visualizations and encapsulates different threading policies.