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The Single Most Important Equation for Brain-Computer Information Interfaces

The Single Most Important Equation for Brain-Computer Information Interfaces

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The Kullback-Leibler Divergence Equation for Brain-Computer Information Interfaces The Kullback-Leibler equation is arguably the best place for starting our thoughts about information theory as applied to Brain-Computer Interfaces (BCIs), or Brain-Computer Information Interfaces (BCIIs). The Kullback-Leibler equation is given as: We seek to express how well our model of reality matches the real system. Or, just as usefully, we seek to express the information-difference when we have two different models for the same underlying real phenomena or data. The K-L…

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The 1-D Cluster Variation Method (CVM) – Simple Application

The 1-D Cluster Variation Method (CVM) – Simple Application

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The 1-D Cluster Variation Method – Application to Text Mining and Data Mining There are three particularly good reasons for us to look at the Cluster Variation Method (CVM) as an alternative means of understanding the information in a system: The CVM captures local pattern distributions (for an equilibrium state), When the system is made up of equal numbers of units in each of two states, and the enthalpy for each state is the same (the simple unit activation energy…

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Biologically-Based Multisensor Fusion for Brain-Computer Interfaces

Biologically-Based Multisensor Fusion for Brain-Computer Interfaces

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Multisensor Fusion for Brain-Computer Interfaces (BCIs) More than 25 years ago, sensor fusion was identified as a militarily critical technology. (See blog post describing role of sensor fusion for Navy air traffic control.) Since that time, both our knowledge of – and the importance of – sensor fusion has grown substantially. Groundbreaking work by Barry Stein and M. Alex Meredith, at the Bowman Grey School of Medicine at Wake Forrest University, elucidated the specific mechanisms of biological sensor fusion in…

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Statistical Mechanics – Neural Ensembles

Statistical Mechanics – Neural Ensembles

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Statistical Mechanics and Equilibrium Properties – Small Neural Ensembles Statistical Mechanics of Small Neural Ensembles – Commentary on Tkačik et al. In a series of related articles, Gašper Tkačik et al. (see references below) investigated small (10-120) groups of neurons in the salamander retina, with the purpose of estimating entropy and other statistical mechanics properties. They provide the following interesting results: Simple scheme for entropy estimation in undersampled region (1), given that only a small fraction of possible states can…

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Statistical Mechanics, Neural Domains, and Big Data

Statistical Mechanics, Neural Domains, and Big Data

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How Neural Domain Activation and Statistical Mechanics Model Interactions in Large Data Corpora (Big Data) I was enthralled. I could read for only a few pages at a time, I was so overwhelmed with the insights that this book provided. And I was about twenty-five years old at the time. I had just discovered this book while browsing the stacks as a graduate student at Arizona State (ASU). The book was The Mindful Brain: Cortical Organization and the Group-Selective Theory…

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Visualizing Variables with the 2-D Cluster Variation Method

Visualizing Variables with the 2-D Cluster Variation Method

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Cluster Variation Method – 2-D Case – Configuration Variables, Entropy and Free Energy Following the previous blog on the 1-D Cluster Variation Method, I illustrate here a micro-ensemble for the 2-D Cluster Variation Method, consisting of the original single zigzag chain of only ten units (see previous post), with three additional layers added, as shown in the following Figure 1. In Figure 1, we again have an equilibrium distribution of fraction variables z(i). Note that, as with the 1-D case,…

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Visualizing Configuration Variables with the 1-D Cluster Variation Method

Visualizing Configuration Variables with the 1-D Cluster Variation Method

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Cluster Variation Method – 1-D Case – Configuration Variables, Entropy and Free Energy We construct a micro-system consisting of a single zigzag chain of only eight units, as shown in the following Figure 1. (Note that the additional textured units, with a dashed border, to the right illustrate a wrap-around effect, giving full horizontal nearest-neighbor connectivity.) In Figure 1, we have the equilibrium distribution of fraction variables z(i). Note that the weighting coefficients for z(2) = z(5) = 2, whereas…

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Why Nonadditive Entropy Is Important for Big Data Corpora Combinations

Why Nonadditive Entropy Is Important for Big Data Corpora Combinations

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Non-Additive Entropy – A Crucial Predictive Analysis Measure for Data Mining in Multiple Large Data Corpora Statistical mechanics has an important role to play in big data analytics. Up until now, there has been almost no understanding of how statistical mechanics provides both practical value and a theoretic framework for data analysis and even predictive intelligence (sometimes called predictive analysis). This blogpost focuses on a related – and crucially important – issue: How can we determine the value of combining…

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Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Data, Big Graphs, and Graph Theory: Tools and Methods

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Big Graphs Need Specialized Data Storage and Computational Methods {A Working Blogpost – Notes for research & study} Processing large-scale graph data: A guide to current technology, by Sherif Sakr (ssakr@cse.unsw.edu.au), IBM Developer Works (10 June 2013). Note: Dr. Sherif Sakr is a senior research scientist in the Software Systems Group at National ICT Australia (NICTA), Sydney, Australia. He is also a conjoint senior lecturer in the School of Computer Science and Engineering at University of New South Wales. He…

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GPUs, CPUs, MIPS, and Brain-Based Computation

GPUs, CPUs, MIPS, and Brain-Based Computation

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GPUs, CPUs, MIPS, and Brain-Based Computation Quick links to useful diagrams: Michael Galloy has produced a good chart showing increase in GPU vs CPU processing over this past decade; nicely continues the line of thought about nonlinear increases in processing power. Look at: http://michaelgalloy.com/2013/06/11/cpu-vs-gpu-performance.html See also post by Karl Rupp: http://www.karlrupp.net/2013/06/cpu-gpu-and-mic-hardware-characteristics-over-time/ Also, this post by NVIDIA: http://http.developer.nvidia.com/GPUGems2/gpugems2_chapter29.html For detailed discussion (including appropriate algorithms/methods), but NOT figures, see: http://pcl.intel-research.net/publications/isca319-lee.pdf Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput Computing…

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