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Category: Computational Neuroscience

Brain Networks and the Cluster Variation Method: Testing a Scale-Free Model

Brain Networks and the Cluster Variation Method: Testing a Scale-Free Model

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Surprising Result Modeling a Simple Scale-Free Brain Network Using the Cluster Variation Method One of the primary research thrusts that I suggested in my recent paper, The Cluster Variation Method: A Primer for Neuroscientists, was that we could use the 2-D Cluster Variation Method (CVM) to model distribution of configuration variables in different brain network topologies. Specifically, I was expecting that the h-value (which measures the interaction enthalpy strength between nodes in a 2-D CVM grid) would change in a…

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The Cluster Variation Method: A Primer for Neuroscientists

The Cluster Variation Method: A Primer for Neuroscientists

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Single-Parameter Analytic Solution for Modeling Local Pattern Distributions The cluster variation method (CVM) offers a means for the characterization of both 1-D and 2-D local pattern distributions. The paper referenced at the end of this post provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 1-D and 2-D pattern distributions expressing structural and functional dynamics in the brain. The equilibrium distribution of local patterns, or configuration…

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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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