Psychophysical identity and free energy

Area of Science:

• Computational neuroscience
• Theoretical biology
• Statistical physics

Background:

• Variational Bayesian inference is a powerful tool for approximating complex probability distributions.
• Understanding the brain's computational principles requires linking its physical processes to information processing.
• Existing theories often struggle to bridge the gap between physical energy and information-theoretic concepts.

Purpose of the Study:

• To propose a framework where thermodynamic free energy directly represents variational free energy in biological systems.
• To investigate the implications of this framework for neuronal encoding in the brain.
• To explore philosophical connections between this approach and existing mind-brain theories.

Main Methods:

• Developing a theoretical approach that equates thermodynamic free energy with variational free energy.
• Analyzing the constraints imposed by this equivalence on neuronal population codes for representing probability densities.
• Drawing parallels with philosophical concepts like mind-brain identity and psychophysical isomorphism.

Main Results:

• The proposed approach necessitates a stochastic population code for neuronal representation of generative and recognition densities.
• A direct mathematical link is established between thermodynamic and variational free energies.
• The findings resonate with philosophical ideas suggesting a deep connection between mental states and brain processes.

Conclusions:

• The framework provides a novel perspective on implementing variational Bayesian inference in biological systems, particularly the brain.
• The requirement for stochastic population codes offers testable predictions for neuroscience.
• This work bridges computational neuroscience, statistical physics, and philosophy of mind, suggesting a unified view of physical and mental phenomena.