Metal nanostructures show pronounced electromagnetic resonances that arise from localized surface plasmons. These collective oscillations of free electrons in the metal give rise to confined electromagnetic near fields. Surface-enhanced spectroscopy exploits the near-field intensity to enhance the optical response of nanomaterials by many orders of magnitude. Plasmons are classified as bright and dark depending on their interaction with far-field radiation. Bright modes are dipole-allowed excitations that absorb and scatter light. Dark modes are resonances of the electromagnetic near field only that do not couple to propagating modes. The suppressed photon emission of dark plasmons makes their resonances spectrally narrow and intense, which is highly desirable for enhanced spectroscopy as well as storing and transporting electromagnetic energy in nanostructures. The suppressed absorption, however, prevents us from routinely exploiting dark modes in nanoplasmonic systems. I propose using spatially patterned light beams to excite dark plasmons with far-field radiation. By this I mean a beam profile with varying polarization and intensity that will be matched to the dark electromagnetic eigenmode. My approach activates the excitation of dark modes, while their radiative decay remains suppressed. I will show how to harvest dark modes for surface-enhanced Raman scattering providing superior intensity and an enhancement that is tailored to a specific vibration. Another feature of dark modes is their strong coupling to the vibrations of nanostructures. I will use this to amplify vibrational modes and, ultimately, induce phonon lasing. The proposed research aims at an enabling technology that unlocks a novel range of nanoplasmonic properties. It will put dark plasmons on par with the well-recognized bright modes to be used in fundamental science and for applications in analytics, optoelectronic, and nanoimaging.

Recent breakthroughs in comparative neurobiological research highlight specific features of the connectivity structure of the human brain, which open new perspectives on understanding the neural mechanisms of human-specific higher cognition and language. In delineating the material basis of human cognition and language, neurobiologically founded modelling appears as the method of choice, as it allows not only for ‘external fitting’ of models to key experimental data, but, in addition, for ‘internal’ or ‘material fitting’ of the model components to the structure of brains, cortical areas and neuronal circuits. This novel research pathway offers biologically well-founded and computationally precise perspectives on addressing exciting hitherto unanswered fundamental questions: How can humans build vocabularies of tens and hundreds of thousands of words, whereas our closest evolutionary relatives typically use below 100? How is semantic meaning implemented for gestures and words, and, more specifically, for referential and categorical terms? How can grounding and interpretability of abstract symbols be anchored biologically? Which features of connectivity between nerve cells are crucial for the formation of discrete representations and categorical combination? Would modelling of cognitive functions using brain-constrained networks allow for better predictions on brain activity indexing the processing of signs and their meaning? This project will use novel insights from human neurobiology translated into mathematically exact computational models to find new answers to long-standing questions in cognitive science, linguistics and philosophy. Models replicating structural differences between human and non-human primate brains will help delineate mechanisms underlying specifically human cognitive capacities. Key experiments will validate critical model predictions and new neurophysiological data will be applied to further improve the biologically-constrained networks.