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NeoCortical Repository and Reports: Database and Reports for NCS Edson O. Almachar, Alexander M. Falconi, Katie A. Gilgen, Devyani Tanna, Nathan M. Jordan, Roger V. Hoang, Sergiu M. Dascalu, Laurence C. Jayet Bray, Frederick C Harris, Jr. Brain Computation Lab Department of Computer Science and Engineering University of Nevada, Reno Outline Introduction Background Design Overview Conclusion and Future Work Human Brain Neurons : ~ 8.6 x 10^10 (86 Billion) Synapses: ~ 1x 10^14 (100 Trillion) Brain Background Neuron ( C ) - cell that uses electrical signals to send information, as well as process it Axon ( A) - the nerve fiber that a neuron’s electric pulse flows through Brain Background Synapse - the transmission of information from one neuron to another Network - a computational model of a cluster of neurons sending information Neural Simulators Allow users to create systems of neurons with parameterized cell data and connection information Simulate brain activity using biological and mathematical models Build a foundation for more research on the processes of the brain Levels of Organization of Modeling What is NCS? Developed and maintained by the UNR Brain Computation Laboratory The NeoCortical Simulator is designed for modeling large-scale neural networks and systems Can model millions of neurons in real time Open source Runs on a heterogeneous cluster of CPUs and NVIDIA GPUs First simulator to support real-time neurorobotics application Building Better Solutions Users are usually researchers in the neuroscience field. User Inconveniences for Neural Simulators Learning to code brain models Time spent organizing output data Generally Low Usability Building Better Solutions Building Better Solutions The Primary Users Neuroscientists Design Goals The Primary Usage Research Simplicity Usability Learnability Easy Collaboration Fast Brain Model Database Design Three Neuron Model Types Necessary Capabilities Izhikevich, Leaky-Integrate-And-Fire, Hodgkin Huxley Storage, Searching, Updating Storage Structure JSON format, Using MongoKit Brain Model Database Design Reports Design Graph Types Understandable Real Time Reporting Customization Raster Plot, Line Graph Color, Size, Type, Neuron Selection Ability to Easily Save Reports Framework FLASK : python microframework MongoDB : nonrelational database D3.Js : Graphing Library jQueryUI.JS : javascript UI library NCR Database Goals Increased Collaboration Simple Layout Easy Searching Database Tab Components Database Model Preview Headers Sorting Feature for Quick Searching Listed in Ascending or Descending Order Simple Preview Information Database Tab Components Left Search Panel Collapsable Grouping Structure Can Select Entire Types Specify Parameter Values As Value or Range of Values Database Tab Database Tab Components Detailed View Opens when model preview is selected Report Tab Goals Management Control Panel Dynamic Creation & Deletion Ability to Save Reports Reports Tab Components Raster Plots Reports Tab Components Line Graphs Reports Tab Components Customizations Color Picker Drag and Drop Scale Axis Reports Tab Components Customizations Cell Selection Pause and Playback Reports Tab Components Saving Reports Image: GIF or SVG Animation: Animated GIF Conclusion Web Application aims to make using NCS easy, Leading to more time spent on research Future Work Complete full front end application by merging NCB with NCR and Virtual Robot NCB Brain Builder Simulation Builder NCR Reports Model Database Virtual Robot NeoCortical Repository and Reports: Database and Reports for NCS Edson O. Almachar, Alexander M. Falconi, Katie A. Gilgen, Devyani Tanna, Nathan M. Jordan, Roger V. Hoang, Sergiu M. Dascalu, Laurence C. Jayet Bray, Frederick C Harris, Jr. Brain Computation Lab Department of Computer Science and Engineering University of Nevada, Reno 30 Hodgkin-Huxley Neurons (Added in NCS 7.0) Biologically accurate Developed in 1952 by Alan Hodgkin and Andrew Huxley from their experiments on the giant axon of a squid Set of four differential equations Three variables n, m, h Hodgkin-Huxley (cont) Leaky Integrate-and-Fire Comprised of Sub-threshold leaky-integrator dynamic Firing threshold Reset mechanism Leakage Channels Drive the neuron to higher voltage Let the voltage decay to its resting potential Izhikevich Created by Eugene M. Izhikevich Published in 2003 Most Simplistic Computationally efficient and captures large variety of response properties of real neurons Only 6 variables! Izhikevich (Added in NCS 6.0) Image Source: Izhikevich Output