Predictive Entropy Maximization performs competitive blind source separation using only local error-driven and Hebbian updates derived from a surrogate entropy objective with spectral error bounds.
Bell and Terrence J
5 Pith papers cite this work. Polarity classification is still indexing.
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Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
A new NMF variant estimates integer and non-integer temporal shifts plus stretching in the frequency domain to improve brain tissue delineation in emission tomography data.
Optimal LPC networks achieve near-minimal response times without trade-offs in energetic cost or robustness, and modular structures with reduced lateral connections match all-to-all networks in performance.
A preprocessing pipeline for resting-state and motor-task EEG is described to support future machine learning models that predict treatment efficacy in chronic neck pain.
citing papers explorer
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Normative Networks for Source Separation via Local Plasticity and Dendritic Computation
Predictive Entropy Maximization performs competitive blind source separation using only local error-driven and Hebbian updates derived from a surrogate entropy objective with spectral error bounds.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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Shift- and stretch-invariant non-negative matrix factorization with an application to brain tissue delineation in emission tomography data
A new NMF variant estimates integer and non-integer temporal shifts plus stretching in the frequency domain to improve brain tissue delineation in emission tomography data.
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Response time of lateral predictive coding and benefits of modular structures
Optimal LPC networks achieve near-minimal response times without trade-offs in energetic cost or robustness, and modular structures with reduced lateral connections match all-to-all networks in performance.
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A Machine Learning Framework for EEG-Based Prediction of Treatment Efficacy in Chronic Neck Pain
A preprocessing pipeline for resting-state and motor-task EEG is described to support future machine learning models that predict treatment efficacy in chronic neck pain.