Abstract
There are numerous examples that show how the exploitation of the body’s physical properties can lift the burden of the brain. Examples include grasping, swimming, locomotion, and motion detection. The term Morphological Computation was originally coined to describe processes in the body that would otherwise have to be conducted by the brain. In this paper, we argue for a synergistic perspective, and by that we mean that Morphological Computation is a process which requires a close interaction of body and brain. Based on a model of the sensorimotor loop, we study a new measure of synergistic information and show that it is more reliable in cases in which there is no synergistic information, compared to previous results. Furthermore, we discuss an algorithm that allows the calculation of the measure in non-trivial (non-binary) systems.
Highlights
There are numerous examples that show how the exploitation of the body’s physical properties can lift the burden of the brain
The state of the actuators and the Umwelt are not directly accessible to the cognitive system, but the loop is closed as information about both the Umwelt and the actuators are provided to the controller by the system’s sensors. In addition to this general concept, which is widely used in the embodied artificial intelligence community, we introduce the notion of world to the sensorimotor loop, that is, the system’s morphology and the system’s Umwelt
We proposed two measures MCW and MCA that are based on calculating the conditional mutual information in the sensorimotor loop [15]
Summary
There are numerous examples that show how the exploitation of the body’s physical properties can lift the burden of the brain. Examples are the spine-driven robot [7], which uses the spine dynamics as part of its controller and the dynamics of an octopus arm that can be used for computation [25] Within this first approach, there are several works that discuss the importance of a tight body–brain–environment coupling, of which the following are just a few examples [13,26,27,28,29,30,31]. Section two discusses in detail the relation between synergistic information and morphological computation, based on previous work and the causal model of the sensorimotor loop. The fourth section presents numerical results which are discussed in the final section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
