Drosophila Acquires a Long-Lasting Body-Size Memory from Visual Feedback.

Grasping an object or crossing a trench requires the integration of information on the operating distance of our limbs with precise distance estimation. The reach of our hands and step size of our legs are learned by the visual feedback we get during our actions. This implicit knowledge of our peripersonal space is first acquired during infancy but will be continuously updated throughout our whole life [1]. In contrast, body size of holometabolous insects does not change after metamorphosis; nevertheless, they do have to learn their body reaches at least once. The body size of Drosophila imagines can vary by about 15% depending on environmental factors like food quality and temperature [2]. To investigate how flies acquire knowledge about and memorize their body size, we studied their decisions to either refrain from or initiate climbing over gaps exceeding their body size [3]. Naive (dark-reared) flies overestimate their size and have to learn it from the parallax motion of the retinal images of objects in their environment while walking. Naive flies can be trained in a striped arena and manipulated to underestimate their size, but once consolidated, this memory seems to last for a lifetime. Consolidation of this memory is stress sensitive only in the first 2 h after training but cannot be retrieved for the next 12 h. We have identified a set of intrinsic, lateral neurons of the protocerebral bridge of the central complex [4, 5] that depend on dCREB2 transcriptional activity for long-term memory consolidation and maintenance.

Integrative Biomimetics of Autonomous Hexapedal Locomotion.

Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots-ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model-we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.