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Hidden Quantum Processes, Quantum Ion Channels, and 1/ fθ-Type Noise
Alan Paris1, Azadeh Vosoughi2, Stephen A Berman3
1NeuroLogic Laboratory, Institute for Simulation and Training, University of Central Florida, Orlando, FL 32826, U.S.A. atparis@knights.ucf.edu.
Neural Computation
|March 23, 2018
Summary
This study analyzes Lorentzian noise in neurological membranes, identifying kinetic eigenvectors as the source of 1/f-type noise. Quantum models offer a new framework for understanding membrane stochastics and noise generation.
Area of Science:
- Neuroscience
- Biophysics
- Quantum Mechanics
Background:
- Lorentzian noise is prevalent in neurological membranes, with its origins in ion channel kinetics.
- Understanding the source of 1/f-type noise is crucial for deciphering neural signaling.
Purpose of the Study:
- To perform a complete analysis of Lorentzian noises in neurological membranes.
- To identify the source of 1/f-type noise.
- To introduce a quantum mechanical formulation for membrane stochastics.
Main Methods:
- Analysis of Lorentzian noise properties, including autocovariance and weighting coefficients.
- Investigation of kinetic eigenvector rotations.
- Introduction and validation of hidden quantum activated-measurement models.
- Application of maximum entropy principles under constrained activation energy.
Main Results:
- Autocovariance of Lorentzian noise depends on eigenvalues (time constants) of the kinetic matrix.
- Lorentzian weighting coefficients depend on eigenvectors, which can be rotated to yield 1/f-type spectra.
- Quantum models are probabilistically indistinguishable from classical models but offer novel insights.
- Maximizing entropy under constrained activation energy reproduces 1/f-type Lorentzian weights.
Conclusions:
- Kinetic and membrane properties can plausibly generate 1/f-type noise, contrary to some literature.
- A realistic, experimentally testable explanation for spectral exponent values is provided.
- Quantum membranes offer applications beyond 1/f-noise, including animal models and quantum foundations.
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