EBRAINS is a collaborative European Research Infrastructure designed to advance and accelerate progress in neuroscience and brain health. This innovative infrastructure, a legacy of the Human Brain Project, is an ecosystem where researchers, clinicians and experts from various disciplines converge to explore and analyze brain complexity – from molecular and cellular levels to the functioning of the entire organ. The EBRAINS 2.0 project aims to create a new standard for brain atlases, link foundational multi-level data and connectomes in the healthy and pathological brain with atlases and models, create digital twins through modelling and simulation as well as unique, excellent, and preferred services for findable, accessible, interoperable, and reusable neuroscience data.

The overarching goal of EBRAINS 2.0 is to foster a deeper understanding of brain structure and function with dedicated and mature software tools, to facilitate the development of more effective treatments, new drugs, diagnostics and preventive measures for neuro-psychiatric disorders. EBRAINS 2.0 will catalyzes progress in the field of large-scale models running on HPC towards Exascale and leads to innovative solutions for neuro-inspired computing, and cognitive technologies such as neurorobotics and AI. Sophisticated digital modeling and data analytics capabilities will benefit communities beyond neuroscience, such as biomedicine.

The project will advance EBRAINS technology, platform services and the base infrastructure roadmap, educate and train a new community of users and developers from academia, industry and SMEs, and ensure knowledge transfer. EBRAINS 2.0 will become the neuroscience hub in the European infrastructure landscape and will make a strong contribution to the new era of digital neuroscience.

The Department of Electronics, Information and Bioengineering, Politecnico di Milano participates in providing use cases as demonstrators, illustrating the contribution of modeling and simulation services to EBRAINS. Namely, it will develop a use case in neurorobotics settings, integrating bioinspired spiking neural networks to perform sensorimotor prediction based on error-driven learning.