• S1 - Spreading of Women in Science and of actions to overcome connected stereotypes
• L1 - Unraveling the Complexity and Causality in the brain, if even possible. A tormented story from Granger to Sugihara
• L2 - Non-linear methods to characterize brain dynamics
• L3 - Brain networks: why, what, how - and how not?
• L4 - Effect of data leakage in brain MRI classification using 2D convolutional neural networks
• L5 - Joint informational and topological signatures of individuality and age
• L6 - Quantifying the dynamics of complex neural systems
• L7 - Modelling the inter-areal cortical network based on a distance rule
• L8 - Ontological framework for EEG analysis based upon multivariate matching pursuit
• L9 - Exploring the visual system with functional digital twins and inception
• L10 - Variability in the analysis of a single neuroimaging dataset by many teams
• L11 - Freeness in cognitive science
• L12 - A hitchhiker’s guide to computational topology - a new lens into the brain
• L13 - Dynamical Criticality and Griffiths Phases in Models of Large Connectomes
• L14 - Aspects of human memory and Large Language Models
• L15 - Timescales in neuronal activity (and how to find them)
• L16 - Higher order informational circuits in neuroscience
• L17 - Complexity & Criticality (in the brain and beyond):  two sides of the same coin
• L18 - Neurons multiplex assembly membership during motor behavior
• L19 - Inferring Neural Activity Before Plasticity: A Foundation for Learning Beyond Backpropagation
• L20 - The role of neuropeptides in shaping neuronal activity in hippocampus
• L21 - 

Abstracts

• W1 - Study of properties and dynamics in trained recurrent neural networks (RNNs)
• W2 - Applying highly comparative time-series analysis to neural time-series data
• W3 - Brain criticality
• W4 - From signal to insight: Introduction to EEG data analysis
• W5 - Communicability, geometry and navigation in (brain) networks
• W6 - Multifractal analysis of time series: concepts, methodology, and practical issues

Abstracts