ACM Transactions on Graphics (TOG)
Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D shock wave visualization on unstructured grids
Proceedings of the 1996 symposium on Volume visualization
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Shock and vortex visualization using a combined visual/Haptic interface
Proceedings of the conference on Visualization '00
Animation and rendering of complex water surfaces
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Image based flow visualization
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Lagrangian-Eulerian advection for unsteady flow visualization
Proceedings of the conference on Visualization '01
Efficient Streamline, Streamribbon, and Streamtube Constructions on Unstructured Grids
IEEE Transactions on Visualization and Computer Graphics
Visualization of Calcium Activity in Nerve Cells
IEEE Computer Graphics and Applications
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Analyzing Complex FTMS Simulations: a Case Study in High-Level Visualization of Ion Motions
IEEE Transactions on Visualization and Computer Graphics
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Waves are a fundamental mechanism for conveying information in many physical problems. Direct visualization techniques are often used to display wave fronts. However, the information derived from such visualizations may not be as central to an investigation as an understanding of how the location, structure and time course of the wave change as key experimental parameters are varied. In experimental data, these questions are confounded by noise and incomplete data. Recognition of waves in networks of neurons is additionally complicated by the presence of long-range physical connections and recurrent excitation. This paper applies visual techniques to analyze the structural details of waves in response data from the turtle visual cortex. We emphasize low-cost visualizations that allow comparisons across neural data sets and variables to reconstruct the choreography for a complex response.