The research area “Core technologies” focuses on the development of novel, multifunctional tools for interfacing with the brain. Microsystems engineering and computer science play a vital role in this pillar of the cluster’s work. The tools that will be developed in this area will constitute the interfaces of choice for clinical studies performed within research area C (the applications pillar of the cluster), as well as for treatments of movement disorders, stroke, and epilepsy. But these tools will also acquire neuronal data that will fuel the analyses and modeling efforts in research area A (the foundations pillar). Thus, the tools will contribute to consolidating the cluster’s basic research in neuroscience. The tools will be designed to be deployed in various brain regions, from the surface of the brain to deep-lying structures. In terms of the spatial resolution, the tools will be capable of covering the range from 50 µm to 5 mm – called the “mesoscopic scale”. Within this pillar, the cluster will address a number of concrete research topics, but will generally focus on the development of tools for monitoring neuronal activity. This includes high-resolution and large-area electrode arrays for recording neuronal signals, microcoil array systems for high-resolution fMRI studies, and tools for in situ optical coherence tomography and spectroscopy. These devices will be complemented by another range of tools that go beyond recording signals. These will modulate neuronal activity, i.e. they will stimulate or inhibit the neurons in their target areas. The ways to achieve this are diverse, and electrical, chemical and optical methods will be utilized. The latter method will play an increasingly important role, as the area of optophysiology – in which modified cells can be controlled in their behaviour by the application of light as a stimulus – is rapidly expanding. The next step will be to turn these tools into stable, adaptive, and robust interfaces, that use advanced algorithmic methods for selecting and interpreting signals and translating them into complex robotic actions. Finally, we will contribute methods and devices to power these technical components, enable efficient signal processing, data handling, and communication. And of course, integration techniques will be required to merge all of these components into complete systems. In conclusion, the overall effort is directed at turning the platforms LiNC and SEAM into reality.