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Interactive Cerebellum Neuron Model Simulation with BOSS and Brain Visualizer Technology

DESCRIPTION

This project features the BOSS brain organization simulation system, which models neuron networks comprising up to 131 billion synapses using the Izhikevich neuron model. Implemented on IBM BlueGene/L V7, the system allows for interactive visualization of the cerebellum, displaying neuron somas, synapses, and synaptic fields. Users can navigate the model with pan, rotate, and zoom features, customizing display options for neuron types and connections. Future developments aim to improve usability and enhance computational efficiency while reducing memory usage.

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Interactive Cerebellum Neuron Model Simulation with BOSS and Brain Visualizer Technology

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  1. Brain Visualizer Remy Oukaour, CSE 511

  2. Brain Organization Simulation System BOSS models neuron networks with up to 131 billion synapses Runs on an IBM BlueGene/L V7 uses the Izhikevich neuron model

  3. INIT Synaptic field bounding boxes of Purkinje, granule and Golgi cells [1] The INIT frontend creates models of the cerebellum for BOSS to simulate

  4. Brain Visualizer Interactively renders INIT models Displays neuron somas, synapses, and synaptic fields Written in C++usingOpenGL’sGLUT and GLUIlibraries

  5. Performance Handles thousands of neurons with tens of thousands of synapses

  6. Performance Speed has been improved from the previous version No longer uses 100% CPU when idle Memory use has only been slightly reduced

  7. Navigation User can pan, rotate, and zoom neuron model They can also select a group of neurons to focus on, while the overview window shows the whole model

  8. Display options Particular types of neuron can be hidden Connections between somas, synapses, and intermediary (via) points can be toggled Colors are configurable

  9. Synaptic fields Displays bounding boxes of synaptic fields

  10. Future development • Fix bugs with displaying connections • Improve UI for ease of use • Reduce memory to allow more neurons and synapses • Don’t load neurons that are off-screen or too far away to be distinguished • Store less data for each neuron and synapse

  11. References [1] Zito, J., Memelli, H., Horn, K., Solomon, I., and Wittie, L. (2012). "Application of a "Staggered Walk" Algorithm for Generating Large-Scale Morphological Neuronal Networks." Computational Intelligence and Neuroscience Volume 2012 (2012), Article ID 876357, 8 pages doi:10.1155/2012/876357.

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