Representation Of Sensory Data Research Articles

160 Applying the Free Energy Principle to Surgical Robotics

Abstract Introduction In an effort to build intelligent, autonomous systems, robotics has repeatedly used neuroscience as a source of inspiration for perception and control. This corresponds well with advances in neuroscience, particularly the free energy principle; a unified theory of the brain, integrating together notions of action, perception, and learning. Crucially for robotics, it provides a simple recipe to synthesise autonomous agents under the framework of active inference. Method We conducted a literature search assessing the practicality of autonomous agency within surgical robotics, and to understand the potential of applying the free energy principle to surgical robotics in a bid to optimise locomotion, spatial awareness, planning, and artificial curiosity. Results Numerous real-life examples demonstrate the feasibility of applying bayesian inference to humanoid robotics. Here we demonstrate a similar approach to adapting surgical robotics through the representation of sensory data through a probability density function over a group of unknown variables. In this manner, surgical robotics will be able to utilise sensory signals and can provide different weights to sensory cues, thereby efficiently integrating sensory input over space and time. Conclusions The free energy principle may be applied to surgical robotics to provide additional feedback, and control to improve patient prognosis.

Neural representation of sensory data

In the target article Pylyshyn revives the spectre of the “little green man,” arguing for a largely symbolic representation of visual imagery. To clarify this problem, we provide precise definitions of the key term “picture,” present some examples of our definition, and outline an information-theoretic analysis suggesting that the problem of addressing data in the brain requires a partially analogue and partially symbolic solution. This is made concrete in the ventral stream of object recognition, from V1 to IT cortex.