Inaugural Lectures | Gioele La Manno and Martin Schrimpf
- 17:15 - 17:25 Introduction
- 17:25 - 17:55 Prof Gioele La Manno
- 18:00 - 18:10 Introduction
- 18:10 - 18:40 Prof Martin Schrimpf
- 18:40 - 18:45 Closure
- 18:45 Apéritif
Gioele La Manno - Unveiling the Architecture of the Developing Brain: Molecular Maps and Methodological Advances
Abstract: More than a century ago, Ramón y Cajal began exploring the complexity of the brain, armed only with a microscope and a pen. Today, technological advancements give us access to an unparalleled level of detail: we are able to read out the molecular composition of single cells. Utilizing single-cell and spatial techniques, my laboratory has contributed to cataloging the brain's fundamental components. We have produced a comprehensive map that chronicles neural development from the earliest stages to birth. To better understand the process, we developed computational methods revealing changes in space and time. Our new techniques let us extend our explorations to other important molecular players in development. Going forward, we aim to discover interactions between genes and lipids that are important for correct brain formation and could be exploited to alleviate neurodevelopmental conditions.
Bio: Gioele La Manno, is a computational and developmental neurobiologist who leads the EPFL Laboratory of Brain Development and Biological Data Science. His training is in Biotechnology and Biomedicine, he earned his PhD with Sten Linnarsson at the Karolinska Institute. He is known for categorizing the progenitor cells in the brain and for the invention of the RNA Velocity Analysis. He recently made advances in the field of lipidomics and co-discovered lipid-based states. Currently, his lab focuses on the role of gene-lipids interactions in brain formation and teratogenesis. His work earned him the Vasco Sanz, EMPIRIS, SIB, and Chorafas awards and the appointment as the first ELISIR scholar.
Martin Schrimpf - Vision and Language in Brains and Machines
Abstract: While modern machine learning originated from the study of the brain and mind, it has long departed from focusing on the human implementation to intelligence. Today's AI models are thus argued by many to be inconsistent with biology. However, empirical evidence suggests that the latest models converge to surprisingly brain-like solutions. Specifically, across a battery of neural and behavioral data, we find that the models that perform best at solving ecological tasks -- such as visual object categorization and next-word prediction -- are also the models that best align with natural intelligence. We are further closing the gap to biology with neuroanatomical models that generalize as well as humans. With these models, we can guide experiments: they generate visual or linguistic inputs that predictably control neural activity, and predict the behavioral effects of neural interventions. Taken together, I will argue that we can understand the human brain and mind in engineering terms.
Bio: Martin Schrimpf is an assistant professor at the EPFL Neuro-X institute, with appointments in the School of Computer and Communication Sciences and the School of Life Sciences. His research focuses on a computational understanding of brain-like intelligence. Following degrees from TUM, LMU and UNA, Martin worked at Siemens, Salesforce, and Harvard before completing his PhD at MIT. He co-founded Integreat which is now helping newcomers in every sixth city in Germany. His work has been featured in Science Magazine, MIT News, and Scientific American. He has been awarded the Neuro-Irv Open Science Prize, the Walle Nauta Award for Continuing Dedication in Teaching, and the Takeda fellowship in AI + Health.
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- Deanship SV, Deanship IC, Brain Mind Institute, Neuro-X Institute
- Manuelle Mary