Transcript Reports Tab Components - Computer Science & Engineering

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
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Introduction
Background
Design Overview
Conclusion and Future Work
Human Brain
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Neurons : ~ 8.6 x 10^10 (86 Billion)
Synapses: ~ 1x 10^14 (100 Trillion)
Brain Background
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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
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Synapse - the transmission of
information from one neuron
to another
Network - a computational
model of a cluster of neurons
sending information
Neural Simulators
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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?
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Developed and maintained by the UNR Brain
Computation Laboratory
The NeoCortical Simulator is designed for
modeling large-scale neural networks and systems
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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
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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
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Building Better Solutions
Building Better Solutions
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The Primary Users
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Neuroscientists
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Design Goals
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The Primary Usage
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Research
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Simplicity
Usability
Learnability
Easy Collaboration
Fast
Brain Model Database Design
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Three Neuron Model Types
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Necessary Capabilities
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Izhikevich, Leaky-Integrate-And-Fire, Hodgkin
Huxley
Storage, Searching, Updating
Storage Structure
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JSON format, Using MongoKit
Brain Model Database Design
Reports Design
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Graph Types
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Understandable Real Time Reporting
Customization
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Raster Plot, Line Graph
Color, Size, Type, Neuron Selection
Ability to Easily Save Reports
Framework
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FLASK : python microframework
MongoDB : nonrelational database
D3.Js : Graphing Library
jQueryUI.JS : javascript UI library
NCR Database Goals
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Increased Collaboration
Simple Layout
Easy Searching
Database Tab Components
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Database Model Preview Headers
Sorting Feature for Quick Searching
 Listed in Ascending or Descending Order
 Simple Preview Information
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Database Tab Components
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Left Search Panel
Collapsable Grouping Structure
 Can Select Entire Types
 Specify Parameter Values
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As Value or Range of Values
Database Tab
Database Tab Components
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Detailed View
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Opens when model preview is selected
Report Tab Goals
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Management Control Panel
Dynamic Creation & Deletion
Ability to Save Reports
Reports Tab Components
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Raster Plots
Reports Tab Components
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Line Graphs
Reports Tab Components
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Customizations
Color Picker
 Drag and Drop
 Scale Axis
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Reports Tab Components
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Customizations
Cell Selection
 Pause and
Playback
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Reports Tab Components
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Saving Reports
Image: GIF or SVG
 Animation: Animated GIF
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Conclusion
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Web Application aims to make using NCS
easy, Leading to more time spent on research
Future Work
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Complete full front end application by merging
NCB with NCR and Virtual Robot
NCB
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Brain Builder
Simulation Builder
NCR
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Reports
 Model
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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)
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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
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Comprised of
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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
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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