Bayesian mechanics of perceptual inference and motor control in the brain

Chang Sub Kim

doi:10.1007/s00422-021-00859-9

Abstract

The free energy principle (FEP) in the neurosciences stipulates that all viable agents induce and minimize informational free energy in the brain to fit their environmental niche. In this study, we continue our effort to make the FEP a more physically principled formalism by implementing free energy minimization based on the principle of least action. We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim in Neural Comput 30:2616–2659, 2018, https://doi.org/10.1162/neco_a_01115) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton’s equation of motion and subject to the control signal arising from the brain’s prediction errors at the proprioceptive level. To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations.

Highlights

The free energy principle (FEP) in the field of neurosciences rationalizes that all viable organisms cognize and behave in the natural world by calling forth the probabilistic models in their neural system—the brain—in a manner that ensures their adaptive fitness (Friston 2010a)
We consider the agent’s locomotion for action inference only implicitly: our formulation focuses on the The Bayesian filtering formalism of the FEP adopts the conimplementation of the control signal at the cept of the generalized motion of a dynamical object by neural level of description and not at the behavioral level defining its mechanical state beyond position and velocity of biological locomotion; the additional mini
This feature is in contrast to the conventional implementation of the FEP, which delivers the backward prediction—belief propagation—as neural dynamics and the forward prediction error as an instant message passing without causal dynamics (Friston 2010a; Buckley et al 2017)

Summary

Introduction

The free energy principle (FEP) in the field of neurosciences rationalizes that all viable organisms cognize and behave in the natural world by calling forth the probabilistic models in their neural system—the brain—in a manner that ensures their adaptive fitness (Friston 2010a). We consider the agent’s locomotion for action inference only implicitly: our formulation focuses on the The Bayesian filtering formalism of the FEP adopts the conimplementation of the control signal (or commands) at the cept of the generalized motion of a dynamical object by neural level of description and not at the behavioral level defining its mechanical state beyond position and velocity of biological locomotion; the additional mini- (momentum). A crucial difference between the two approaches is that while the gradient descent scheme searches for an instantaneous trajectory representing a local minimum on the IFE landscape in the multidimensional generalized state space, our theory determines an optimal trajectory minimizing the continuoustime integral of the IFE in two-dimensional phase space for a single variable problem

Treatment of noise correlations

Closure of the sensorimotor loop in active inference

Closed-loop dynamics of perception and motor control

Simple Bayesian-agent model: implicit motor control

Concluding remarks

Compliance with ethical standards