Seminars

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.

Jiaqi Zhang examines how computational models can learn the effects of genetic interventions from single-cell experiments. Such experiments reveal causal relationships, but their high-dimensional measurements are costly to collect and difficult to interpret. The seminar connects identifiable causal representations with a predictive method for previously unseen perturbations. The approach incorporates prior biological knowledge and changes in data distributions to estimate responses at individual-cell resolution. It also uses predictions to guide subsequent experiments. An application identifies and experimentally validates previously unknown T-cell regulators with potential relevance to cancer immunotherapy. The recording follows the original July seminar; the series lists Zhang at MIT, while the recording biography describes her incoming Columbia appointment.

Benoit Assi examines limits on reliable theoretical predictions for present and future particle colliders. Low-order simulations of QCD radiation carry large uncertainties, hadronization is commonly modeled rather than derived, and effective theories contain more operators than measurements can constrain. Information theory and machine learning can incorporate improved calculations into simulations, quantify uncertainty, choose informative observables and advance hadronization theory. Geometry of effective-theory field space combines infinite operator families into finite physical quantities, while information measures identify the combinations experiments can resolve. The talk develops these complementary approaches to extracting the available information from LHC and future-collider data.

Claudio Manzari connects astrophysical observations and precision flavor measurements to two unresolved aspects of CP symmetry. For the strong CP problem, the talk considers QCD axions produced in a supernova core and converted to gamma rays by surrounding magnetic fields. A Galactic supernova could expose this distinctive transient, motivating the GALAXY network of telescopes monitoring the full sky. The second part examines the origin of flavor-sector CP violation through precise measurements of the CKM unitarity triangle. Possible hints of spontaneous CP violation are discussed alongside their implications for quark flavor and the fundamental symmetries of matter.

Ania Liu examines gravitational-wave lensing as a probe of compact objects, dark matter and cosmic large-scale structure. Propagation over cosmological distances can distort signals, but identifying lensing in observations requires disentangling those distortions from uncertain waveform models and other astrophysical effects. The talk describes this phenomenology and the ambiguities it creates for interpretation, then presents recent methods and results from lensing searches using LIGO–Virgo–KAGRA data.

Arka Banerjee introduces k-nearest-neighbor distributions as summaries of cosmological survey data that capture information beyond two-point statistics. They respond to moments of all N-point correlations while retaining a computational cost comparable to two-point measurements. The talk covers auto-correlations and cross-correlations in discrete and continuous datasets, their relationship to other higher-order summaries, and applications that improve detection significance or parameter constraints. It also explores modeling these distributions with methods already successful for two-point functions in real and redshift space.

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.

Tien-Tien Yu examines observational signatures of ultralight scalar dark matter with quadratic couplings to Standard Model fields. The couplings change fundamental constants over time, affecting primordial light-element production, the microwave-background power spectrum and tests of the equivalence principle. The talk follows these effects from the early Universe to present experiments and explains how current cosmological measurements constrain this class of scalar-dark-matter models.

Subha Majumdar presents a six-dimensional Milky Way phase-space catalogue designed to map stellar motions and dark matter beyond the reach of Gaia parallaxes alone. Gaia astrometry is combined with spectrophotometric distances and radial velocities from fourteen surveys, including DESI, SDSS-BOSS, APOGEE and LAMOST. The resulting catalogue contains about 33 million tracers and detailed distance–velocity measurements for roughly half a million halo stars. It characterizes distant clusters, dwarf galaxies and stellar streams, enabling mass modeling, kinematic studies, abundance mapping, Galactic archaeology and dark-matter searches. An application constructs a continuous Galactic rotation curve extending to 250 kiloparsecs.

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.

Giada Santoro examines strong-field gravity and compact-object structure using GW250114, a gravitational-wave event with signal-to-noise ratio around eighty. The unusually strong signal supports precise tests of departures from general relativity and detailed measurements of black-hole ringdown. No tidal deformability is detected: the analysis places a ninety-percent upper bound of 34.8 on the effective tidal-deformability parameter. This is consistent with the vanishing tidal response predicted for Kerr black holes.

Networks of interconnected neurons display diverse patterns of activity. Relating these patterns to the structure of the network is a central goal of theoretical neuroscience. Classic neural field and rate models have been powerful tools for this purpose due to their analytical tractability. Here, we show that the recently-developed combinatorial threshold-linear network (CTLN) model is a mean-field theory for excitatory-inhibitory Hawkes networks, with clustered connectivity, in an inhibition-stabilized regime. This mapping allows us to leverage powerful analytical results for CTLN networks to predict diverse macroscopic dynamics of clustered Hawkes networks, including metastability between various macroscopic fixed points, limit cycles, and chaotic attractors. We will then examine an extension of this approach to models with nonlinear dendritic dynamics, focusing on dendritic calcium spikes.We uncover a marked point process mean-field theory for these n etworks and use this to examine how somatic vs dendritic-targeting connectivity shapes the mean-field equilibrium phase diagram. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-05-13. Recording duration: 00:54:14.

Carolina Paiva develops exact matrix product states for strongly interacting electrons in lattice bands. Conformal-field-theory correlation functions already provide exact representations of fractional quantum Hall trial wavefunctions, including Laughlin states. Extending the construction to fractional Chern insulators is obstructed by the lattice length scale. The talk shows how ideal Chern bands overcome that obstruction and derives an exact representation of Laughlin model states in a hybrid Wannier basis on a torus.

Seth Koren shows how global symmetry structure and field-space topology affect particle-physics predictions beyond the usual analysis of small field fluctuations. Different possible global forms of the Standard Model gauge group imply different model-independent predictions for fractionally charged particles. Collider searches for these particles could identify the gauge-group structure and exclude unification models. The talk then examines axion theories, using the DFSZ model to show how the global properties of scalar fields and gauge symmetries modify axion strings and can resolve the cosmological domain-wall problem.

Anna Tsai studies how precise pulsar distances can improve localization of individual gravitational-wave sources with pulsar timing arrays. A coherent map-making method uses distance information to approach the diffraction limit, potentially reaching angular precision of about two arcminutes and enabling electromagnetic counterpart searches. At a signal-to-noise ratio of ten, approximately nine pulsars can reach this limit. The resolution improves sharply as more well-timed pulsars have accurately known distances. Since the distance of PSR J0437−4715 is already measured to subparsec precision, the talk motivates coherent analyses that fully incorporate pulsar-distance information.

Katarzyna Swirydowicz uses an asynchronous solver on an AMD system to examine the practical implementation of computational linear algebra across CPUs and GPUs. The talk introduces the relevant computational ideas, programming models, and software tools, then considers how algorithmic structure interacts with hardware capabilities. This case study illustrates both the opportunities and implementation challenges of asynchronous methods for large-scale scientific computing on GPU-accelerated systems.

Yousef Saad examines relaxed gradient descent for large-scale optimization. Relaxing the optimal step length in Cauchy's steepest descent avoids its characteristic zigzag behavior and can bring the search direction close to an eigenvector of the Hessian. Once that alignment is sufficiently accurate, properties of the Lanczos method can accelerate convergence. The talk analyzes several such strategies and illustrates them in global minimization of strictly convex functions, retaining the simplicity and low memory requirements that make gradient methods attractive for machine learning.

Neural oscillations are often proposed to support brain computation by routing information, organizing cell assemblies, or shaping coding dynamics. Yet these ideas usually assume rhythms that are strong, sustained, and regular, whereas in vivo oscillations are often weak, transient, noisy, and variable in frequency and phase. In this talk, I will argue that such “no-metronome” oscillations are not just noisy fluctuations, but coordinated complex dynamics with functional consequences. Combining analyses of neural activity recordings during actual behavior (mice and non-human-primate LFPs and human EEG) with computational modelling, I will discuss evidence that transient oscillatory events can carry task-relevant information and support flexible communication through spatiotemporally structured relationships across populations, timescales, and frequencies. Together, these results suggest that oscillatory weakness and weirdness are not just imperfections, noise to average-out, but part of the functional repertoire of neural computation Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-05-06. Recording duration: 00:40:47.

Matthias Bolten discusses the scalability of multigrid solvers for linear systems arising from discretized partial differential equations. On modern supercomputers, heterogeneous CPUs and GPUs and the widening gap between computation, network, and memory speeds complicate parallelization. Classical multigrid analysis relies on tightly coupled multiplicative components, whereas additive and asynchronous variants relax this coupling. The talk compares approaches to improving high-performance multigrid scalability, including asynchronous execution.