Computational Neuroscience

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.

Annual conference of the Bernstein Network Computational Neuroscience, bringing together students, postdocs and PIs from around the world to meet and discuss new scientific discoveries in computational neuroscience. Satellite workshops Sep 28-29, main conference Sep 29-Oct 1 at Goethe University, Campus Westend, Frankfurt am Main.

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.

The Fortieth Annual Conference on Neural Information Processing Systems brings together interdisciplinary machine-learning research through peer-reviewed sessions, invited talks, demonstrations, tutorials, workshops, and an exposition.

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.

The Stagkourakis Lab at Karolinska Institutet is recruiting a postdoctoral scholar to study neural circuits governing survival and homeostatic behaviors. The project combines systems neuroscience, Neuropixels recordings, imaging, circuit manipulation and transcriptomics in the SciLifeLab and Department of Neuroscience research environment.

This NIH BRAIN Initiative funding opportunity supports new or substantially advanced theories, mechanistic or predictive models, and computational or statistical methods that improve quantitative understanding of brain function across scales. Tools must address complex neural and behavioral data and be made broadly available to the neuroscience research community.

Postdoctoral research with Christian Doeller at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. The group studies structured representations supporting navigation, episodic and working memory, learning and decision-making. Projects connect hippocampal population coding with knowledge representations and possible translation to Alzheimer’s disease. Approaches include high-field fMRI, MEG, EEG, virtual reality, psychophysics and computational models, including machine learning and deep neural networks. The project can be shaped around interdisciplinary interests. Applicants need a PhD in cognitive neuroscience, psychology, biology, computer science, physics or a related field, strong programming skills, an excellent academic record and interest or experience in computational modelling. The September 2026 Max Planck Postdoc Program offers at least three years of employment and structured career development. Apply through the programme portal by 13 October 2026 at 12:00 Europe/Berlin (CEST). Create an account to access Apply and submit the required CV with publications, previous-research summary and statement of interest, following the portal’s full instructions.

Lead the behavioural-evaluation component of Mariya Toneva’s ERC BrainAlign project. The postdoc will develop tasks and a gamified platform to collect human understanding of full narratives and books, then evaluate brain-aligned language models against those data. The role combines NLP, machine learning and cognitive science, with collaboration across computational and neuroimaging teams. This vacancy belongs to the September 2026 Max Planck Postdoc Program call, which accepts applications until 13 October 2026 at 12:00 CEST through the official application platform.

Postdoctoral research with Christian Doeller at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. The group studies structured representations supporting navigation, episodic and working memory, learning and decision-making. Projects connect hippocampal population coding with knowledge representations and possible translation to Alzheimer’s disease. Approaches include high-field fMRI, MEG, EEG, virtual reality, psychophysics and computational models, including machine learning and deep neural networks. The project can be shaped around interdisciplinary interests. Applicants need a PhD in cognitive neuroscience, psychology, biology, computer science, physics or a related field, strong programming skills, an excellent academic record and interest or experience in computational modelling. The September 2026 Max Planck Postdoc Program offers at least three years of employment and structured career development. Apply through the programme portal by 13 October 2026 at 12:00 Europe/Berlin (CEST). Create an account to access Apply and submit the required CV with publications, previous-research summary and statement of interest, following the portal’s full instructions.

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.

OCNS has announced that its annual Computational Neuroscience meeting will take place in Gran Canaria on 10–14 July 2027. The meeting is a specialist forum for theoretical and computational approaches to nervous systems. This is an advance planning listing: the announcement does not yet provide a 2027 registration or abstract-submission window. Consult OCNS for the forthcoming programme and participation details.

Lead the behavioural-evaluation component of Mariya Toneva’s ERC BrainAlign project. The postdoc will develop tasks and a gamified platform to collect human understanding of full narratives and books, then evaluate brain-aligned language models against those data. The role combines NLP, machine learning and cognitive science, with collaboration across computational and neuroimaging teams. This vacancy belongs to the September 2026 Max Planck Postdoc Program call, which accepts applications until 13 October 2026 at 12:00 CEST through the official application platform.