This workshop will explore how brain digital twins are revolutionizing research into pathological brain conditions and transforming the landscape of precision medicine. Participants will learn how these models work and how they integrate data and tools from different fields, such as molecular neuroscience, network theory and dynamical systems. We will discuss how digital twins can help identify early biomarkers able to characterize pathological states and predict disease progression. Another key topic will be the use of digital twins as in silico environments for testing potential treatments before applying them in clinical scenarios.
Through real-world examples and interactive sessions, we will tackle some of the challenges that come with this innovative approach, such as achieving anatomical precision, handling large datasets, and ensuring ethical use in patient care. The focus will remain on making these cutting-edge tools accessible and impactful, not just for researchers but also for clinicians aiming to deliver more effective, tailored care to their patients.

Aims and topic
Predictive processes are ubiquitous in the brain and thought to be critical for adaptive behaviours, such as rapid learning and generalisation of tasks and rules. Early works such as the computational vision model proposed by Rao and Ballard (1999) have inspired over two decades of theoretical, computational, and experimental research about predictive neural processing. Stemming from these early works, ongoing investigations provide a rich ecosystem of theory, experiments and computational models that expand beyond the notion of predictive coding. Further, thanks to rapidly developing neural recording technologies, large datasets at multiple scales of granularity and resolution are becoming increasingly available. New computational models enable us to gain a mechanistic understanding of how neural circuits learn to implement and deploy predictive computations. Yet, a full understanding of the underlying computational logic remains fleeting because different aspects are often studied in separate research programs (e.g., layer circuits vs whole-brain neuroimaging), with little cross-pollination. This symposium will look at predictive processing in the context of modern computational neuroscience. Speakers will discuss new theories extrapolating low-dimensional population activity, recent work exploring efficient coding in artificial neural networks and rats' visual cortex, coding hierarchies of prediction errors across brain areas, and computational modelling of behaviour and neural data across species (humans, monkeys, rodents), focusing on high-level, flexible behaviours (hierarchical reasoning, context changes, conceptual knowledge). The topic addressed in this symposium is central to multiple streams of research in computational neuroscience, e.g., perception, decision-making, motor control, and social behaviour. Our aspiration is to stimulate interaction among researchers working in different disciplines and highlight the open questions that will shape future research.

Speakers

Matthias Tsai --Bern University, Switzerland
Rohan Rao --Newcastle University / Oxford University, UK
Erin Rich --New York University, USA
Silvia Maggi --University of Nottingham, UK
Armin Lak --Oxford University, UK

Schedule
9.00 - 9.05: opening remarks
9.05 - 9.45: Erin Rich,
9.45 - 10.15: Rohan Rao,
10.15 - 10.30: coffee break
10.30 - 11.00: Matthias Tsai
11.00 - 11.45: Aurelio Cortese
11.45 - 12.00: discussion
12.00 - 14.00: Lunch
14.00 - 14.45: Abhishek Banerjee,
14.45 - 15.30: Silvia Maggi
15.30 - 15.45: coffee break
15.45 - 16.30: Armin Lak,
16.30 - 17.00: discussion
17.00 - 17.05: closing remarks