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| Home > Publications database > Brain models as digital twins advance theory and neuromorphic Computing |
| Talk (non-conference) (Invited) | FZJ-2025-03438 |
2024
Abstract: Computational neuroscience is entering a new era. This originates from the convergence of two developments: First, biological knowledge has expanded, enabling the construction of anatomically detailed models of one or multiple brain areas. Second, simulation has firmly established itself in neuroscience as a third pillar alongside experiment and theory. A conceptual separation has been achieved between concrete network models and generic simulation engines. Neuroscientists can now work with digital twins of certain brain structures to test their ideas on brain functions and probe the validity of approximations required for analytical approaches.However, the use of this capability also requires a change in mindset. Computational neuroscience seems stuck at a certain level of model complexity for the last decade not only because anatomical data were missing or because of a lack of simulation technology. The fascination of the field with minimal models leads to explanations for individual mechanisms, but the reduction to the bare equations required provides researchers with few contact points to build on these works and construct larger systems with a wider explanatory scope. In addition, creating large-scale models goes beyond the period of an individual PhD project. The change of perspective required is to view digital twins as research platforms and scientific software as infrastructure.As a concrete example, the presentation discusses how the universality of mammalian brain structures motivates the construction of large-scale models and demonstrates how digital workflows help to reproduce results and increase the confidence in such models. A digital twin promotes neuroscientific investigations but can also serve as a benchmark for technology. The talk shows how a model of the cortical microcircuit has become a de facto standard for neuromorphic computing [5] and has sparked a constructive race in the community for ever larger computation speed and lower energy consumption.
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