3D Brain Dendrites Visualization

3D Biological Interactive Visualization of Brain Neurons, Dendrites and Synapses

Dimitris Mitakis


Katerina Mania

Research Domain
• Visualization
• Biology
• Neuroscience


The large amount of information accumulated over the years and the research on the human – or animal – brain, requires simulations and ways to represent the data. These are necessary both for scientists and non-specialized users, in order to understand better the functions and the way that the brain manages any information it acquires. In the contexts of this diploma thesis, an application has been designed in order to visualize the information obtained after a neural network simulation. The implementation of the Neural Network Simulation is detailed in the diploma thesis titled “Neural Network Simulation Speedup based on Reconfigurable Logic”. The application also enables the input of simulation data, thus acting – in combination with the software system for simulation – as a comprehensive study tool and neural network representation. Due to the fact that biologists who work in the neurobiology field are using standard forms for displaying exit data of a simulation, in this thesis a different approach is being tested. This approach constitutes an effort on the imaging of a non-photorealistic scene, based on scientific discoveries and microscopic photos. In opposition with the current forms of displaying neuron data – such as tables, networks with nodes, graphs – a three-Dimensional space is visualized, by using the game engine Unity. Three-dimensional biological models coexist with some of the forms of displaying, as mentioned above. Two forms of visualization are implemented. The first is based on the idea of raster-plot, but it is a little bit more complicated and more analytic, as it contains the value of cells and dendrites action potential. The second form of visualization was based on the idea of the creation of a three-dimensional world, with three-dimensional neuron cells models in it, and graphs with each neuron and dendrite action potential.