Morphological Computation: Synergy of Body and Brain

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.

Computing the Unique Information

“…We hope that our algorithm will contribute means to test the mutual information decomposition on larger systems than was possible so far, particularly in recent applications of the decomposition, e.g., in neuroscience (Pica et al 2017), representation learning (Steeg et al 2017, Tax et al 2017, robotics (Ghazi-Zahedi and Rauh 2015, Ghazi-Zahedi et al 2017), etc., which so far has been pursued either with only simpler types of measures or for very low-dimensional systems.…”

When the goal is to generate a series of activities: A self-organized simulated robot arm

“…The attractors self-stabilizing in the sensorimotor loop may then give rise to complex patterns of regular and of chaotic motion primitives [15], which can be selected in a second step using 'kick control' [16]. From a general perspective, kick control is an instance of a higher-level control mechanism exploiting the reduction in control complexity provided by morphologically computing robots [17,18]. These approaches are hence different from other works where closed-loop policies are applied on the top of open-loop gait cycles [19,20].…”

Toward Computing with Spider Webs: Computational Setup Realization

“…Hauser et al presented two theoretical frameworks for the concept [17,18], realized in robotic research [15,16,22], where the highly non-linear reservoir in a reservoir computing paradigm is substituted with a complaint body, reducing a complex problem of dynamic filter design or system limit-cycle control to dynamic learning of a set of linear weights. Ghazi-Zahedi et al have investigated quantitative measures for morphological computation in continuous and discrete systems [23,24]. A summary of the most current state of the art, definitions, and examples of morphological computation is presented by Füchslin et al [25].…”