Computational Neuroscience

Postdoctoral position in the Siegert Group developing mathematical and computational methods for analyzing complex cellular morphology in the MIND-MAP project, an interdisciplinary collaboration studying microglia dynamics and brain function. Position closes once a suitable candidate is found.

The goal of the Neural Dynamics accelerator is to understand how the brain generates flexible behavior. We aim to uncover the algorithms—implemented by dynamics in brain-wide neural circuits—that allow animals to build internal models, process information, and choose actions. We are searching for a Scientist to develop data-driven theories of how circuit structure shapes neural computation. The…

Work with Professor Lida Kanari at the Mathematical Institute on the MRC-funded project Algebraic topology bridging the gap between single neurons and networks. The researcher will conduct independent and collaborative work in applied mathematics and computational neuroscience, participate in the Oxford Centre for Industrial and Applied Mathematics, and publish research findings. Up to three…

The annual Computational and Systems Neuroscience (Cosyne) meeting, an exchange forum for experimental and theoretical/computational approaches to problems in systems neuroscience. Cosyne returns to Montreal, Canada, from 11-16 March 2027.

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.

Surround suppression is ubiquitous in visual processing and perception. Neurophysiology and modeling studies reveal a rich set of spatial context effects in the Primary Visual Cortex, whereby surrounding stimuli nonlinearly modulate (e.g., suppress) the responses of a center target stimulus. Studies suggest that the strength of surround suppression depends on the image statistics of the center and surround pattern. Namely, if the center and surround share similar statistics, the neural responses are more suppressed by the surround. This behavior has been connected to computational benefits such as improving coding efficiency, and relates to highlighting salient image structure. Though Convolutional Neural Networks (CNNs) have been popular models for biological visual systems, it is unknown if they also have such surround suppression effects. We took advantage of the feature visualization method to visualize the surround pattern that is most suppressive to the center pattern. In detail, we came up with a two-step optimization method: first we found the optimal center pattern, and next the most suppressive surround pattern. Our results match the general understanding of surround suppression effects in the visual cortex. We found that the most suppressive surround is visually similar to the center, while the most facilitative surround is visually distinct from the center. We also found that the most suppressive surround can follow the change in the center, i.e. when presenting a non-optimal center. When the center color was altered, the most suppressive surround was also changed to match the center color. More surprisingly, when the center texture was changed entirely, some neurons in deeper layers could still follow the center texture. Our study not only suggests a new view towards the efficiency of CNNs, but also a new experimental paradigm that could be used in future neurophysiology studies.

Maxim Raginsky joins Paul Middlebrooks to examine brains, AI and biological autonomy through control theory. They discuss what behaviour reveals about a system's internal organisation, analog and digital descriptions, the relationship between control and cybernetics, active inference and the limits of computational accounts. The publisher offers a public episode and supporter access to its full archive.

Andrew Huberman joins Patrick O’Shaughnessy to discuss the frontier of neurotechnology and sustained human performance. Their conversation considers the convergence of AI and biotechnology, gene-based methods for changing neural activity and motivation, and the relationships between visual behaviour, arousal and sleep. Huberman is a neuroscientist and professor at Stanford University.

Cristopher Moore connects computational complexity to questions about cognition and artificial intelligence. The discussion considers what makes a problem difficult, how rugged landscapes shape computation, and what complexity theory can contribute to understanding learning and generalization in brains and machines.

Stanford researcher Surya Ganguli connects statistical physics, theoretical neuroscience and machine learning. He discusses scaling laws, data selection and the origins of diffusion models, then turns to neural experiments on perception, self-related processing and describing neuronal responses. The conversation closes with questions about human versus machine data efficiency and the different ways brains and artificial networks acquire useful algorithms. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.