Abstract
When Donald Hebb published his 1949 book ``The Organization of Behavior'' he opened a new way of thinking in theoretical neuroscience which, in retrospective, is very close to contemporary ideas in self-organization. His metaphor of ``wiring'' together what ``fires together'' matches very closely the common paradigm that global organization can derive from simple local rules. While ingenious at his time and inspiring the research over decades, the results still fall short of the expectations. For instance, unsupervised as they are, such neural mechanisms should be able to explain and realize the self-organized acquisition of sensorimotor competencies. This paper proposes a new synaptic law which replaces Hebb's original metaphor by that of ``chaining together'' what ``changes together''. Starting from differential Hebbian learning, the new rule grounds the behavior of the agent directly in the internal synaptic dynamics. Therefore, one may call this a behavior-driven synaptic plasticity. Neurorobotics is an ideal testing ground for this new, unsupervised learning rule. This paper focuses on the close coupling between body, control, and environment in challenging physical settings. The examples demonstrate how the new synaptic mechanism induces a self-determined ``search and converge'' strategy in behavior space, generating spontaneously a variety of sensorimotor competencies. The emerging behavior patterns are qualified by involving body and environment in an irreducible conjunction with the internal mechanism. The results may not only be of immediate interest for the further development of embodied intelligence. They also offer a new view on the role of self-learning processes in natural evolution and in the brain. Videos and further details may be found under \url{http://robot.informatik.uni-leipzig.de/research/supplementary/NeuroAutonomy/}.
Highlights
Autonomy is a puzzling phenomenon both in the evolution of species and in individual development
I will formulate this rule for the case of a flat sensorimotor loop as introduced in Section “BehaviorDriven Differential Hebbian Learning.”
Well aware of the no-free-lunch theorem, I discuss in Section “Spontaneous Symmetry Breaking – the Pattern Behind the Patterns” the general phenomenon of spontaneous symmetry breaking, explaining how low-dimensional behavioral modes may emerge in high-dimensional systems seemingly out of nothing
Summary
Autonomy is a puzzling phenomenon both in the evolution of species and in individual development. Formulated at the level of behavior, those general principles may be translated into specific rules acting in the internal world of the agent Different from such a top-down way of thinking, this paper presents a bottom-up approach, claiming that there exist specific internal mechanisms that, while being unspecific for any task or survival strategy, per se have the ability to guide systems to selfdetermined activity. I will formulate this rule for the case of a flat sensorimotor loop as introduced in Section “BehaviorDriven Differential Hebbian Learning.” This minimalist control paradigm rests on the conviction that control should be less a prescription of what the robot is to do, but consists more in the excitation of specific modes emerging from the irreducible coupling of the mechanical system (robot + environment) with the nervous system. Well aware of the no-free-lunch theorem, I discuss in Section “Spontaneous Symmetry Breaking – the Pattern Behind the Patterns” the general phenomenon of spontaneous symmetry breaking, explaining how low-dimensional behavioral modes may emerge in high-dimensional systems seemingly out of nothing
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