Vinod Menon examines the brain's default mode network as a foundation for autobiographical memory, self-related processing, and consciousness, and considers how these functions compare with current artificial intelligence systems. The virtual session is part of Stanford's Contemplation by Design Summit.

Gašper Tkačik explores whether the language of information in biology can become a predictive scientific theory. The lecture connects information transfer from DNA to proteins, positional signals that guide cell fate during development, neural information processing, and the storage and inheritance of information in evolving genomes. It brings physics, information theory and quantitative biology…

Arno Onken examines transformer models for predicting responses in the retina and visual cortex. These models learn complex neural-response patterns from large datasets and can transfer learned representations between settings, but fitting and interpreting them differs from linear–nonlinear and convolutional approaches. The talk discusses the opportunities and difficulties this creates for…

The BrAIN group recruits a postdoctoral researcher for the ERC-funded BrainAlign project, investigating whether human brain activity can improve language models’ long-timescale understanding. The researcher leads the computational neuroscience work, integrates heterogeneous fMRI datasets and develops models that learn from naturalistic language comprehension. The project also uses these models to…

Design and conduct behavioural and neuroimaging studies of learning-related brain plasticity at the Center for Lifespan Psychology. The project tests the EESR theory through motor-skill learning and analyses of brain change. Responsibilities include study design, advanced statistical analysis, manuscripts and scientific presentations. The Berlin appointment is initially for three years at 39…

Jin Zhu examines statistical approaches to identifying text produced by large language models. The increasing availability of fluent generated writing raises questions about misinformation, academic integrity and the authenticity of digital content. Although detection has attracted substantial machine-learning research, the talk argues that its statistical foundations remain underdeveloped. Zhu…