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.
author Huxley, A.F
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A windowed active set algorithm solves large-scale Lasso for convolutional spike sorting with linear complexity in temporal dimension and parallel efficiency.
Phenomenological modeling with observables for ion currents, temperature effects, and inductance provides a hybrid framework that brings mathematical descriptions of action potential propagation in axons closer to biological reality.
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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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Large scale Lasso with windowed active set for convolutional spike sorting
A windowed active set algorithm solves large-scale Lasso for convolutional spike sorting with linear complexity in temporal dimension and parallel efficiency.
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On phenomenology of physical effects in axons
Phenomenological modeling with observables for ion currents, temperature effects, and inductance provides a hybrid framework that brings mathematical descriptions of action potential propagation in axons closer to biological reality.