The ongoing search to decode the brain communication system has propelled major advancements in bioelectronics research. From these, planar microelectrode arrays (MEAs) offer the possibility to record neuronal signals for extremely long periods of time and to probe fundamental principles of learning and adaptation. Unfortunately, MEAs have low neuronal coupling and are thus insensitive to sub‐threshold neuronal signals such as post‐synaptic responses. To surpass this major limitation, a novel category based on 3D high aspect ratio microstructures has emerged. In particular, gold‐mushroom microelectrode arrays (GMμEAs) have appeared as a solution. Their unique morphology, promoting cell engulfment without penetrating the neuron, prevents the occurrence of cell repairing mechanisms common in other microelectrodes. This new class of recording and/or stimulating devices have opened the door to the vast communication system of neuronal cells by enhancing the cell–microelectrode interface. This review compiles the fundamentals of these promising devices including their fabrication, work principles and focusing on how the different morphological parameters affect the recording performance. Although limitations exist, there is a growing interest to explore the new solutions that GMμEAs still offer, both as an electrophysiological tool (e.g., cell guidance) or in applications such as plasmonics.

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Robotics and Artificial Intelligence are two deeply intertwined fields of study, currently experiencing formidable growth. To foster these developments, the RoboCup initiative is a fantastic test bed to experiment new approaches and ideas. This dissertation is rooted in the groundwork laid by previous FCPortugal3D contributions for the RoboCup simulation 3D robotic soccer league, and seeks to design and implement a humanoid robotic kick system for situations where the robot is moving. It employs Reinforcement Learning (RL) techniques, namely the Proximal Policy Optimization (PPO) algorithm to create fast and reliable skills. The kick was divided into 6 cases according to initial conditions and separately trained for each of the cases. A series of kicks, both static and in motion, using two different gaits were developed. The kicks obtained show very high reliability and, when compared to state of the art kicks, displayed a very high time performance improvement. This opens the door to more dynamic games with faster kicks in the RoboCup simulation 3D league.