Conscious active inference I: A quantum model naturally implements the path integral needed for real-time planning and control.

Active inference is a general framework for optimizing the behavior and learning of a sentient agential system. It may be interpreted as a general theory of sentient behavior and has been used to quantitatively model a wide variety of perceptual and behavioral contexts. Moreover, variables in neural process models of active inference appear to be represented by specific pathways in the brain, and they predict some features of actual neural responses and behavioral patterns in a variety of contexts. These applications support the validity of the active inference framework for describing real animals. However, implementing active inference in a conscious agent requires a system capable of sophisticated probabilistic computations, including a weighted average over its potential future trajectories-a path integral. Although it is straightforward to construct realistic classical biophysical neural models to approximate these computations in simple contexts, we argue in this first of two companion papers that classical Hodgkin-Huxley-style neurons are unlikely to be capable of performing these computations