Seminars

Gašper Tkačik explores whether the language of information in biology can become a predictive scientific theory. The lecture connects information transfer from DNA to proteins, positional signals that guide cell fate during development, neural information processing, and the storage and inheritance of information in evolving genomes. It brings physics, information theory and quantitative biology together to examine these processes across biological scales. Tkačik is Professor at the Institute of Science and Technology Austria. Shinya Kuroda provides commentary; Arisa Ema moderates. Online via Zoom Webinar. Wednesday 14 October 2026, 14:00–15:00 JST (Asia/Tokyo; UTC+9). Public advance registration is required through the organizer’s event page. The lecture is in English with Japanese interpretation. Organized by Tokyo College, The University of Tokyo Institutes for Advanced Study.

Ilaria Ferlenghi discusses a career spanning structural biology, cryo-electron microscopy, vaccine research and development, and the growing role of artificial intelligence and machine learning in scientific innovation and entrepreneurship.

Real-time measurement of local biomolecule concentrations in living tissue requires sensors that are stable, selective and minimally invasive. This seminar examines single-walled carbon nanotubes, whose durable near-infrared fluorescence and sensitivity to their surroundings support optical biosensing. Biopolymer functionalization gives the nanotubes biological compatibility and selectivity for particular molecular targets. Spectroscopy, microscopy and machine-learning-assisted analysis are used to characterize sensor interactions and extract biological information, with applications spanning live-cell imaging, wearable sensing and continuous monitoring.

Clarice D. Aiello examines evidence that quantum effects influence biological function, including magnetic sensing used in animal navigation, cellular metabolism and enzyme activity, and the capture of light energy in photosynthesis. The talk reviews proposed biophysical mechanisms and their implications for human, plant and environmental biology, while considering the difficulty of connecting observations from nanometre scales to whole organisms. A central question is whether quantum mechanics can be established or ruled out as the explanation for physiologically relevant effects, and whether those effects can be controlled for useful applications. Potential directions include biomimetic electromagnetic probes, quantum-computing architectures that operate at room temperature, improved solar-energy devices and new therapies. Progress will require work across disciplines and scales, with theory and experiment developed together.

Paul S. Weiss (UCLA) explores low-energy interactions and balanced cycling in natural systems as models for efficient synthetic devices and recycling. Because the relevant energies fall below visible-photon energies, tunnelling spectroscopy and imaging are used to probe these processes. Replicating biochemical cycling and low-energy transport could reduce the energy needed for devices and materials recovery. The work seeks missing physical principles, including spin conservation in chiral molecules and polarizability in biological and synthetic systems. This perspective has led to thermal-control advances in scale, speed and magnitude. Atomic-resolution imaging, spectroscopy and functional measurements help build precise structures while revealing heterogeneous nanoscale behaviour. The talk relates these advances to the physical, electronic, mechanical, thermal and chemical connections between materials and their surroundings that determine future nanomaterial measurements and devices.

How do humans stabilize an inverted pendulum, and why does a balanced pole eventually fall? Drawing on 25 years of fingertip pole-balancing research, this talk examines neural correction delays: longer poles move more slowly relative to the nervous system’s response time. Delay-differential models can stabilize the upright position, yet skilled people still experience falls. The proposed explanation is microchaos arising from interactions among delay, sensory dead zones and frequency-dependent force encoding. A region of transient falling solutions lies next to stable microchaotic dynamics. Such microchaos is absent in virtual frontal-plane balancing tasks, while models of standing postural sway lack the corresponding transient regime. The comparison suggests that human falls, unlike pole falls, are more plausibly associated with medical events or slips and trips.

To orchestrate complex behaviors in mammals, the cerebral cortex must continuously transmit its processing results to subcortical regions. While internal cortical processing relies on highly selective sparse codes, these descending cortical output streams employ an enigmatic dense code, characterized by high firing rates and low feature selectivity. This dense population code presents a profound paradox: how can downstream regions extract precise, cognitively relevant signals when most cortical output neurons can be active at any moment? We address this paradox by testing the hypothesis that cortex does not lose information when switching to a dense code. Instead, it utilizes a mechanism for sparse-to-dense coding transformations discovered by my laboratory: thalamocortical synapses target specifically the dendritic initiation zone for calcium action potentials (APs), which enables cortical output neurons to transmit multiple information streams simultaneously via a multiplexed 1-2-3 AP syntax. I will provide first evidence that this remarkable synaptic specificity and coding syntax generalize across long-range pathways. Information coupling via an anatomically and biophysically distinct dendritic nexus may hence be a ubiquitous mechanism for sparse-to-dense coding transformations in cortex. Dissecting these mechanistic origins of cortical output streams is only now possible due to a unique in vivo – in silico approach, perfected over two decades, that my laboratory developed for bridging the gaps between dendritic and population-level computations

Richard Huang investigates how DNA nanostructures deform under uneven hydrodynamic forces, a question relevant to drug delivery, vaccines and biosensors. Coarse-grained molecular dynamics and mechanical models first examine linear and circular DNA in extensional flow. Drag concentrates tension near the middle of linear duplexes, disrupting base pairs and causing overstretching. Deforming minicircles straighten along their sides while concentrating bending into opposing tips and localized kinks. Models based on DNA elasticity and hydrodynamic-force distributions explain these responses, then extend to wireframe DNA origami to study force transmission and how local structural transitions produce whole-particle deformation. The results inform structures designed for stability or controlled deformation in flow. Online via Zoom. Friday 2 October 2026, 12:00–13:00 EDT (America/New_York; UTC−4). Open the Zoom link on the official event page; the audience explicitly includes the public. Richard Huang is affiliated with MIT. Organized by MIT Department of Mathematics — Computational Research in Boston and Beyond Seminar.

Cryopreservation constrains manufacturing and clinical delivery of immune cells such as NK and T cells. DMSO protects against freezing but becomes toxic at physiological temperatures; associated infusion morbidity can require washing or divided dosing that diminishes therapeutic effectiveness. This talk presents an alternative in which reversible, cytocompatible hydrogels encapsulate cells before freezing without DMSO. The speaker examines evidence that encapsulation cushions osmotic stress, suppresses kinetic and phase transitions, and reduces injury caused by extracellular ice growth and fusion. The discussion covers recovery efficiency, function after thawing, control of biological stress responses, and the challenges of extending the approach to clinical-scale cell preservation.

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.

Bowen Li explains how ionizable lipids are designed for lipid nanoparticles that deliver messenger RNA. The seminar covers combinatorial and machine-learning-guided design, four-component reactions for rapidly producing lipid libraries, and barcoded screening in living systems to identify formulations targeting organs and cells, including lungs, T cells and tumours. It then examines applications of AI-guided nanoparticle platforms to mRNA therapeutics. Li is an Associate Professor and Canada Research Chair at the University of Toronto's Leslie Dan Faculty of Pharmacy. Online via Zoom. Thursday 24 September 2026, 11:00–12:00 Eastern Daylight Time (America/New_York; UTC−4). Use Join via Zoom on Western University's event page. The stated audience includes researchers, students, alumni and the general public. Organized by Western University's Western Bioinformatics Research Seminar.

Rani Borbara presents brain-wide recordings from more than 150 freely moving C. elegans to examine how anatomical connectivity becomes functional organization during behavior. Cross-animal models map cell-class conditional dependencies and test how synapses, gap junctions, neuromodulation and shared brain states shape circuit dynamics.

Stanford Neurosciences Seminar Series talk by Emery Brown (Massachusetts Institute of Technology, Neuroscience Statistics Research Lab) on deciphering the dynamics of the unconscious brain under general anesthesia, hosted by the Wu Tsai Neurosciences Institute.

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.

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.

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.

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.

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.