Seminars

Mark Harnett presents work on how synaptic organization, nonlinear dendritic processing, and neuronal activity patterns interact to support computation, flexibility, and learning in the adult mammalian neocortex. The Brain and Cognitive Sciences colloquium is followed by a reception.

Florian Marquardt surveys approaches to reducing the energy and computational cost of neural networks by implementing their functions in physical devices. The central question is how experiments can efficiently determine updates to adjustable parameters, allowing the hardware itself to learn. Examples include learning through time reversal, obtaining nonlinear computation from linear optical scattering, and training analog quantum simulators as neuromorphic systems. The seminar is available in person and through the registration link on the organizer’s event page.

Qihao Ye examines practical, affordable ways for individual mathematicians to use AI in literature searches, proof development and verification. The talk identifies seven recurring failure modes and introduces the open-source QED multi-agent system designed to address them. A case study on constructing Carleman weights for wave equations on the half-line shows how automated search can be combined with a researcher’s mathematical insight and rigorous checking. The organizer links both the implementation and the accompanying research paper. Online via Rutgers DIMACS Zoom. Wednesday 23 September 2026, 10:00–11:00 EDT (America/New_York; UTC−4). Use the Zoom link and passcode on the official event page. Organized by the DIMACS AI and Mathematics Seminar. This listing advertises the verified remote attendance option.

MIT Biology Colloquium Series talk by David Bilder (University of California, Berkeley), hosted by Yukiko Yamashita, on invertebrate oncology and the ancient evolutionary origins of tumor-host interactions.

ISTA Lecture by Klaus Robert Müller (TU Berlin & Korea University, Seoul) on how machine learning and AI enable scientific research, particularly in medicine and chemistry, and on explainability techniques for extracting understanding from machine learning models.

Bryce Mander presents research on early sleep biomarkers associated with Alzheimer's disease pathophysiology and memory impairment. The William C. Dement Seminar connects sleep and circadian neuroscience with neurodegeneration, cognition and clinically relevant approaches to detecting disease-related change.

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.

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.

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.

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.

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.

Reward expectations – one’s prediction about the likelihood of future outcomes - play a central role in shaping the satisfaction derived from those outcomes. Most existing research treats expectations as static, assuming they remain fixed in time. However, real-life expectations are often dynamic, fluctuating as new information becomes available. For example, during a soccer game, your expectations of seeing your team winning will likely rise and fall as the game unfolds. In the main part of this talk, I will present a series of studies demonstrating that human expectations can be tracked at sub-second timescales. Using slot machines as a case study, we leverage the continuous deceleration of the reels to elicit moment-by-moment fluctuations in rewardexpectations. To capture these dynamics, we take complementary approaches: we use the high temporal resolution of electroencephalography (EEG) to track neural signatures of evolving expectations, and we develop a novel behavioral paradigm (“Slot or Not”) designed to measure changes in expectations via betting behavior. Across four studies, we show that expectations fluctuate continuously and can be tracked both behaviorally and neurally. Extending these findings, a subsequent intracranial study shows that the human orbitofrontal cortex (OFC) encodes the moment-by-moment changes of reward expectations. In the second part of this talk, I will return to the relationship between expectations andsatisfaction. If expectations shape satisfaction, and if they are best conceptualized as dynamic trajectories rather than static quantities, a key question arises: does the trajectory leading up to an outcome influence how that outcome is evaluated? I will outline a new research direction aimed at formalizing this relationship using computational modeling. This is ongoing work, and I welcome feedback on how best to formalize these ideas. Finally, I will discuss potential extensions of this framework to psychopathology, asking whether alterations in dynamic expectations may characterize conditions such as Major Depressive Disorder and Gambling disorder. Together, this work introduces a new framework for studying expectations as dynamic processes, offering a richer understanding Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-04-29. Recording duration: 00:42:25.