Speaker
Description
Despite remarkable advances in artificial intelligence, modern machine learning systems still face important challenges, including reliable uncertainty estimation, robustness to distribution shifts, and efficient adaptation to new tasks. Biological intelligence offers a valuable source of inspiration for addressing these limitations.
In this talk, I will explore the connections between generative AI and neuroscience, focusing on how the brain combines sensory evidence with prior knowledge to interpret the visual world. I will present our research on brain-inspired generative models, particularly Variational Autoencoders (VAEs), that improve uncertainty estimation, capture hierarchical information processing, and enable efficient adaptation to changing contexts.
Beyond drawing inspiration from biology, these models also provide computational tools for understanding neural activity. In collaboration with experimental neuroscientists, we have demonstrated how context-dependent responses in the mouse visual cortex can be explained through probabilistic inference with adaptable priors.
Our work highlights the potential of a two-way exchange between AI and neuroscience: developing more robust and adaptive machine learning systems while gaining a deeper understanding of biological intelligence.