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Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.

In recent years the robotics community has focused its interest on the control of the impedance of compliant actuators to increase safety and to make the interaction with humans more natural. Biological systems are elastic and are able to modulate joint impedance while keeping stability through co-activation and reciprocal activation of muscles. In order to implement a bio-equivalent, technical drive actuation system for prosthetics, a bio-inspired position and stiffness control strategy has been implemented and connected to a technical model of an elbow joint. The sum of all muscle–torques actuating the joint represents the net-torque that should be generated in the technical elbow to realize the desired motion. This net-torque is transmitted to a miniaturized lightweight joint drive with inherent serial elasticity and controlled with a speed–torque control cascade. The impedance range of the biological musculoskeletal system is evaluated in simulation and compared to the range obtained when the technical drive is acting instead of its biological counterpart. The impedance range of the technical drive using biological controllers is equivalent to that achieved in the biological example. Simulation results of the biologically controlled drive for different load situations show that the system successfully modulates impedance both in static cases and during movements while keeping stability.