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.

Shantanu Jadhav presents work on coordinated hippocampal-prefrontal activity and the neural mechanisms that support memory-guided cognition and decisions. The seminar draws on systems electrophysiology and circuit analysis to connect distributed network dynamics with behavior.

Takaki Komiyama will discuss how cortical circuits maintain information about the value of behavioral options across trials and transform past experience into future choices. The talk draws on longitudinal imaging and circuit manipulation in mice to examine distinct neuronal populations and local and long-range interactions in history-dependent decision-making.

Navigation and memory require coordination between map-like allocentric representations and body-centered egocentric actions. Aaron Wilber examines how the parietal–retrosplenial–anterior thalamic–hippocampal network performs these transformations, and how disrupted coordination contributes to spatial deficits in aging and Alzheimer’s disease. Rat sequence-task experiments reveal bidirectional hippocampal–parietal interactions across route-centered, place and egocentric representations. Because that task does not isolate reference-frame use, the laboratory developed a freely available task separating egocentric, allocentric and transformation conditions. Validation indicates that parietal cortex and anterior thalamus are required for transformation; ongoing work examines population states associated with each condition. The talk then connects impaired hippocampal–cortical communication during sleep to navigation deficits in 3xTg-AD mice, reporting recovery of circuit function and cognition after early, circuit-targeted 40 Hz entrainment. Related investigations examine this network during waking navigation. In TgF344-AD rats, egocentric impairments appear before transformation deficits, addressing the shortage of rodent studies of reference-frame coordination in Alzheimer’s disease.

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

Scott J. Russo examines how chronic psychosocial stress alters communication between the immune system and the brain in depression and anxiety. Human observations and mouse experiments connect persistent increases in circulating monocytes and inflammatory signals with stress-related illness. The talk describes stress-responsive neurons that project to bone marrow and regulate monocyte production, release and trafficking. It also considers how stress disrupts the blood–brain barrier, allowing peripheral inflammatory proteins to affect reward regions such as the nucleus accumbens. Together, these findings examine how peripheral immune cells act at the neurovascular interface to change brain circuits and behaviour, providing potential mechanisms relevant to treatment-resistant mood disorders. Online via Rutgers Brain Health Institute Zoom. Thursday 1 October 2026, 12:00–13:00 EDT (America/New_York; UTC−4). Open the organizer’s event page and follow its Zoom link; the meeting ID and passcode are supplied there. Hosted by Yong Kim as part of the BHI Plenary Seminar Series. This listing advertises the verified remote option.

Nicolas Mathevon examines the evolution of vocal communication through an analysis of songs from more than 3,000 passerine species. The study organizes their acoustic diversity into eight basic motifs and relates motif use to social organization, morphology, mating systems and sound transmission. Tropical forest conditions favor simpler signals such as flat whistles that travel effectively through dense habitat. In temperate regions, dense populations, short breeding seasons and stronger sexual selection favor complex signals such as rapid trills, despite their greater degradation during transmission. The resulting global pattern reflects a changing balance between environmental constraints and selection for complex communication. Online on 30 September 2026, 17:00–18:00 CEST (Europe/Paris; UTC+2), with an optional 15-minute discussion afterward. Open worldwide through Zoom. Use the join information on the organizer's page; its optional registration form subscribes attendees to seminar updates. Organized by LINGUAE's Animal Linguistics Seminar, with Emmanuel Chemla, Lucie Ravaux and Philippe Schlenker. Online via Zoom; public joining instructions on the organizer’s seminar page.

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.

Z Yan Wang presents comparative neuroscience research on the brains and behaviors of octopuses and bumblebees to illuminate life-history processes. The talk is part of Columbia's Tuesdays@10 neuroscience seminar program for researchers across fields and career stages.

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.

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.

Rachna Reddy studies relationships among wild chimpanzees and bonobos to investigate adolescence as a period of both vulnerability and resilience. Drawing on more than a decade observing chimpanzees, the presentation brings together biological, anthropological, psychological and neuroscientific perspectives through cross-species empirical research. It connects questions about development in our closest living relatives with understanding the human life course; Reddy’s fellowship work also includes a coming-of-age novel informed by the lives of the chimpanzees she studies.

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.

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.

Optimal transport is a mathematical method to define a distance between probability distributions. This is particularly useful in various domains, including physics, biology, machine learning, and economics, among others. After introducing the Optimal Transport (OT) problem at finite temperature, we show how it can be formulated as a statistical physics problem. This approach allows us to derive very efficient algorithms to effectively compute the distance between two probability distributions. The a priori unrelated Schrödinger bridge (SB) problem is presented, and it is shown to be a dynamical version of the optimal transport problem. Indeed, the Schrodinger bridge looks for the most probable path in probability distribution space, which connects two given probabilities. The Schrodinger bridge problem, originally devised for freely diffusing particles, can be generalized to the case of interacting particles. It can be formulated in terms of functional integrals over bosonic fields, which allows us to derive partial differential equations that characterize the most probable paths in probability space. CARL VAN VREESWIJK MEMORIAL LECTURE 2026. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-04-15. Recording duration: 00:55:05.