One of the keys towards understanding how the brain works as a whole is visualisation of how the individual cells function; photo-realistic rendering is therefore important.
Ray-tracing can help to highlight areas of the circuits where cells touch each other and where synapses are being created. In combination with ‘global illumination’, which uses light, shadow, and depth of field effects to simulate photo-realistic images, this technique makes it easier to visualise how the neurons function.
Slides of the talk given in Tokyo about interactive ray-tracing in the context of brain visualization can be downloaded from here.
Here is an alpha demo of the application that I am developing for visualising growing neurons. The application allows interactive detection of touches, making it a convenient tool for debugging algorithms used to grow the morphologies.
Currently working on reconstructing neurons and glia cells from 3D points and radius. A combination of metaballs and parametric geometry appear to be the best choice for ray-tracing based fast rendering.
of the limitations of high performance software is that it restricts
itself to high-end machines. In a time of tablets and laptops, other
solutions are needed. The idea is to send information such as mouse and
keyboard events to the server. The server takes care of the rendering
and sends a stream of images back to client. Transport is optimized
using compression technologies, making it possible for every client to
enjoy a different and fully customizable view of the protein. I truly believe that ray tracing is the future of digital imaging and augmented reality.
Being able to go much further than rasterization in terms of image
quality, ray-tracing also makes it easy to compute, for example, the
amount of light received by an object.
nature of ray-tracing, and the techniques used for its implementation
can be reused to run scenarios such as calculating interactions between
atoms or determining what the surface of contact would be between two