Material Basis of Symbols

Understanding the mechanisms by which the structure and function of neuronal populations bring about specific cognitive functions is the aim of the project »Material Basis of Symbols«. Using models of specific parts of the brain, the team aims to engineer and imitate motor function, symbol production and understanding using precise mathematical models of the human cortex. These models, referred to as Brain-Constrained Neural Networks (BCN, see fig. X), encompass multi-level information about neuroanatomical structure and neurophysiological function, including microscopic within- and macroscopic between-area connectivity, interactive dynamics of neurons and neuronal assemblies, and biological learning mechanisms. This work draws on current linguistic theories of semantics and semiotics as well as neurocognitive experiments to develop models of the material basis of symbol processing of different types (e.g., concrete and abstract concepts), which will be archived in close interaction with the MoA project »Symbolic Material« and the ERC Advanced Grant ›Material Constraints Enabling Human Cognition‹.

Furthermore, BCN simulations of symbolic processing will also enable a deep understanding of the brain´s materiality at the cellular/synaptic and at the cortical level, along with its functional dynamics at the large-scale brain matter communications. Such understanding will be the basis for the application of brain-constrained neural networks in light of network-based neurosurgery that opens a new avenue to simulate and explain the functional effects of structural focal lesions so as to predict functional consequences of deprivation- and surgery-related brain structural changes. The ultimate aim is to incorporate specific features of individual healthy or patient brains to model individual differences in anatomy and function, allowing subject-specific predictions on the consequence of brain surgery.