Seminars

Daphna Shohamy and Ashok Litwin-Kumar bridge experimental and computational neuroscience to explain how the brain assigns significance to memories, how dopamine shapes memory-guided decisions, and how bodily signals influence learning. Isabel Low moderates a public discussion and question session.

Shiv Grewal discusses how chromatin and epigenetic mechanisms establish, maintain, and transmit genome states beyond the DNA sequence itself. This NIH Wednesday Afternoon Lecture Series event is offered in person and through NIH VideoCast.

Omar M. Yaghi examines how reticular chemistry changes the scale and diversity of materials that chemists can design. By joining molecular building blocks into extended structures, researchers have developed large families of metal–organic frameworks and covalent organic frameworks with tunable internal spaces and functions. The lecture asks how artificial intelligence and robotics could accelerate the preparation and exploration of these materials, moving chemical discovery from scarcity toward abundance. Yaghi connects this approach to carbon capture and the collection of water from air, building on design principles for nanoporous frameworks and their applications in gas storage and separation. The discussion extends the metal–organic framework research recognised by the 2025 Nobel Prize in Chemistry into current methods for discovery and environmental applications.

Tal Linzen, an associate professor of linguistics and data science at New York University and a research scientist at Google, will speak in MIT's Siegel Family Quest for Intelligence seminar series. His work combines behavioral experiments and computational methods to study language learning and comprehension, alongside large-language-model post-training, evaluation, and interpretability.

This English-language webinar examines advances in organoid research, production, and drug discovery. Hideya Sakaguchi covers the developmental-biology origins of neural organoid research and its potential for drug discovery, followed by a keynote from Thomas Mitchell of The Organoid Company and a live question session.

Yuko Munakata presents evidence challenging capacity-only accounts of self-control and explains how experience, effort, and expected payoff shape executive-function engagement and later outcomes.

Zohar Ringel of the Hebrew University of Jerusalem presents a renormalisation-group approach to deep learning based on neural-network field theories. The seminar examines scaling laws, the removal of non-learnable field modes, benign overfitting and open questions about feature learning. The confirmed Quantum Matter seminar takes place in the Bob Room at Perimeter Institute on 8 September 2026, from 15:30 to 17:00 Toronto time.

Nick Turk-Browne presents research using infant functional MRI to investigate how the hippocampus supports learning and memory early in life. The seminar connects neural development, memory formation and infantile amnesia through new methods for studying awake infants.

Margaret Livingstone of Harvard Medical School presents in Princeton Neuroscience Institute's public seminar series. Livingstone's laboratory studies how visual recognition occurs in the primate brain using behavior, brain imaging, and electrophysiology.

Michelle M. Li examines how computational analyses can preserve the biological context of a proposed treatment, including cell type, disease state, genetic background and patient characteristics. She introduces Medea, an AI system that combines biological software, predictive models and literature retrieval while checking intermediate steps and reconciling evidence. The seminar presents evaluations involving cell-specific target selection, cancer-cell synthetic lethality and immunotherapy response. A separate yeast experiment tests predictions against previously unpublished measurements of gene-pair interactions under DNA-damaging treatments. The research addresses whether an agent can transfer useful evidence between contexts while recognizing when that transfer is unsupported. Reported comparisons cover predictive performance, computational failures and the ability to abstain. This recording retains the original seminar date.

Suhrid Ghosh will present work on how cell mechanics and cytoskeletal dynamics shape the evolution of reproductive structures in Drosophila ovaries. Cross-species morphology, ex-vivo live imaging, perturbations of cortical tension, and newly isolated terminal-filament cells reveal how somatic-cell deformability can alter ovariole architecture and reproductive fitness.

Stanford's summer research symposium features a keynote by Jamie Zeitzer, undergraduate lightning talks, and a poster and demonstration session on human performance research.

Gladstone-UCSF Institute of Genomic Immunology Seminar by Christine Brown, PhD (Deputy Director, T Cell Therapeutics Research Laboratories) on developing and refining redirected CAR T cells for malignant brain tumors, including glioblastoma, tumor heterogeneity, immunosuppressive microenvironments, delivery routes, and manufacturing.

Thesis presentation by Tiffany Zhang, graduate fellow in the Vinogradova Lab at The Rockefeller University, on chemical proteomics approaches revealing post-translational remodeling of endosomal pathways during macrophage activation. Open to the tri-institutional community.

Special seminar at MIT's Picower Institute for Learning and Memory with neuroscientist Hee-Sup Shin, MD, PhD, of the Institute for Basic Science (IBS), held in the Picower Seminar Room (46-3310) at 43 Vassar Street.

MIT researchers and students are invited to two talks on emerging applications of ultrasound for non-invasive brain imaging, neural modulation and wearable human-machine interaction.

Two talks present emerging applications of ultrasound technology in neuroscience, spanning brain imaging and human-machine interaction. The in-person seminar is open to MIT researchers, students, and staff and is supported by the MIT School of Science and the Feng Lab at the McGovern Institute.

Klemens Rottner (Helmholtz Centre for Infection Research, Braunschweig) presents evidence that profilin acts as a master regulator of actin filament dynamics essential for cell migration and growth, controlling both formin- and Arp2/3 complex-dependent actin assembly pathways.

Large recurrent networks are important models in several fields, including neuroscience, machine learning, physics, and applied mathematics. Yet their dynamics are difficult to study directly, because high-dimensional nonlinear systems can exhibit rich behavior that is hard to summarize in terms of individual trajectories. In this talk, I will discuss an approach that seeks to understand such dynamics through the structure of the network’s equilibria. I will focus on a random balanced network of threshold-linear units that undergoes a transition from a single stable equilibrium to extensive chaos as the disorder strength crosses a critical value. Using a combination of Kac–Rice theory, replica calculations, numerical root-finding, and dynamical mean-field theory, we show that the chaotic regime contains an exponentially large number of equilibria. These equilibria are all saddles, but with only a fractionally small number of unstable directions. Surprisingly, despite the completely random connectivity, the equilibria are not scattered randomly through phase space. Instead, they are strongly correlated and confined to a comparatively small region. The chaotic attractor lies within this same region, suggesting a direct geometric link between the organization of unstable equilibria and the collective structure of the dynamics. This picture helps explain why networks with extensive chaos can nevertheless display dynamics dominated by a relatively small number of collective modes. More broadly, the results suggest that the geometry of equilibria provides a useful complementary perspective to dynamical mean-field theory for understanding high-dimensional neural dynamics. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-05-27. Recording duration: 00:46:40.

Traditional work in the study of human reward-based learning involves designing an experimental task---often inspired by Reinforcement Learning (RL) theory---and fits a small set of computational models---often inspired by RL algorithms---to that dataset. For example, researchers often model human behavior on bandit tasks using variants of Q-learning. While this approach has been highly productive, leading to landmark discoveries such as the dopamine reward prediction error hypothesis, it also has limitations. This talk focuses on the lack of generalizability of such models: Even if they closely fit behavior on the original task, models derived from the one-task-one-model paradigm usually predict behavior on other tasks quite poorly. I argue that this lack of generalizability is a fundamental problem for the cognitive sciences: we intuitively expect our models to be robust to superficial task differences, such as variations in the number of choice options, reward probabilities, or the exact kind of non-stationarity. I will propose potential solutions to this problem along two dimensions: the behavioral dataset and the computational model. Regarding computational models, I will introduce work in which we moved beyond the limitations of hand-crafted one-off models by employing flexible, data-driven methods. These methods allowed us to compare classes of models instead of individual model instances, allowing us to cover the space of possible models more exhaustively, and innovate cognitive mechanisms very efficiently. For the behavioral dataset, we move from using single learning tasks to a comprehensive task space that encompasses most existing paradigms in the literature, while closing the gaps between them in a near-continuous fashion. Our results suggest that more general models in conjunction with broader datasets can pave the road toward increasingly general models of human reward-based learning and decision making, and a persistent departure from many aspects of RL theory. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-05-20. Recording duration: 00:51:19.