Ising models for neural activity inferred via selective cluster expansion: structural and coding properties

DOI10.1088/1742-5468/2013/03/P03002zbMath1456.82487OpenAlexW1999677560MaRDI QIDQ3301538

Simona Cocco, John P. Barton

Publication date: 11 August 2020

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1742-5468/2013/03/p03002

Mathematics Subject Classification ID

Neural biology (92C20) Disordered systems (random Ising models, random Schrödinger operators, etc.) in equilibrium statistical mechanics (82B44)

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Uses Software

• glasso
• ElemStatLearn

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• High-dimensional graphs and variable selection with the Lasso
• Small-correlation expansions for the inverse Ising problem

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