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Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology

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The brain of biological systems is a highly complex and very efficient computing unit. It can deal with a multitude of tasks from low-level sensorimotor coordination to high-level cognition. Specifically, it can process high-dimensional sensory information and, dependent on this, generate coordinated motor ...

The brain of biological systems is a highly complex and very efficient computing unit. It can deal with a multitude of tasks from low-level sensorimotor coordination to high-level cognition. Specifically, it can process high-dimensional sensory information and, dependent on this, generate coordinated motor commands in real time, resulting in actions (like, locomotion and manipulation). Simultaneously, it can also perform cognitive functions (such as navigation, goal-oriented behavior, reasoning and decision making, interaction, communication). This amazing performance is achieved by using the full capacity of its neural dynamics, learning, memory, and adaptation as well as by interacting with the environment through its body (i.e., sensory-motor system). Thus, actions and cognition require dynamical brain-body-environment interactions and thereby cannot be disembodied. A number of researchers have tried to investigate biological systems to understand principles underlying these complex behaviors and to imitate such performance with artificial systems by using different types of neural computation models in open-loop and closed-loop contexts. However, achieving the level of performance of living creatures remains a grand challenge. According to this, the Research topic welcomes articles that recent neural mechanisms for the generation of complex behavior in embodied closed-loop systems from biological investigations to technical implementations.


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