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Recognition Dynamics in the Brain under the Free Energy Principle
1Department of Physics, Chonnam National University, Gwangju 61186, Republic of Korea.
Neural Computation
|July 19, 2018
Summary
Organisms minimize environmental uncertainty by minimizing variational free energy. This principle, framed as least action, yields neural recognition dynamics for perception and Bayesian filtering.
Area of Science:
- Computational Neuroscience
- Theoretical Neuroscience
- Bayesian Inference
Background:
- Perception is modeled computationally using the principle of least action.
- The free energy principle posits that organisms minimize sensory uncertainty.
- Autopoiesis provides a framework for understanding self-organizing biological systems.
Purpose of the Study:
- To reformulate the free energy principle using the principle of least action.
- To derive neural recognition dynamics (RD) from variational free energy.
- To demonstrate the utility of RD in biophysical and hierarchical brain models.
Main Methods:
- Formulating computational perception within the principle of least action.
- Defining theoretical action as the time integral of variational free energy.
- Deriving neural recognition dynamics (RD) by taking the variation of informational action.
- Casting free energy minimization into Hamiltonian mechanics.
Main Results:
- Neural recognition dynamics (RD) derived, reducing to Bayesian filtering of external states.
- Free energy minimization recast into Hamiltonian mechanics using positions and momenta of environmental representations.
- RD implementation shown in single-cell biophysical models and hierarchical brain architectures.
- Numerical solutions for RD presented, analyzing perceptual trajectories in neural state space.
Conclusions:
- The principle of least action offers a novel framework for computational perception.
- Neural recognition dynamics provide a unified approach to perception and Bayesian inference.
- The derived dynamics are implementable in biologically plausible neural models at multiple scales.
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