Topic: learning

This UniDistance Suisse event explores how research on socio-emotional skills can inform play, learning and discussion about emotions. It introduces the Emotion board game through a keynote, an interdisciplinary panel and practical activities, in collaboration with the Réseau Émotions et Éducation. Programme on 11 November 2026, Europe/Zurich (CET, UTC+01:00): 13:45 welcome; 13:50–14:05 keynote on experiencing emotions through play with Andrea Samson (UniDistance Suisse) and Nicolas Bressoud (HEP-VS); 14:05 film; 14:15–15:00 panel with Michele Ursprung (Canal9), David Sander (CISA and Univers

Apply through the RobotX programme to several ETH robotics research groups, selecting up to three potential supervisors. Areas include soft and biohybrid robots, locomotion, manipulation, perception, human–robot interaction, learning and safety. Successful candidates receive fully funded doctoral employment at ETH salary rates with social benefits and define their research project with a supervisor. Start by agreement in 2027. Apply through the ETH online recruitment portal. Application deadline: 2026-11-27.

Experimental psychology research on attention, perception, learning, memory, language and reasoning falls within the society’s Quarterly Journal of Experimental Psychology remit. Supports small empirical projects in experimental psychology, including research assistance, participant costs, specialist software, scanning fees, essential small equipment and consumables. The maximum is GBP 10,000 for a project normally completed within one year. Applications are due 1 December 2026, with earliest start 1 February. Funds are reimbursed to the host institution in arrears; overheads, student fees and

Experimental psychology research on attention, perception, learning, memory, language and reasoning falls within the society’s Quarterly Journal of Experimental Psychology remit. Travel and accommodation support for a visit to another institution to learn experimental methods or undertake studies unavailable at the home department. Up to GBP 3,500 is available to the visitor, with up to GBP 100 of additional actual host costs. Subsistence, conference fees and poster costs are excluded. The next submission date is 1 December 2026; visits should start at least three months after submission. Appl

Support for ambitious multidisciplinary foundational computing research, including intelligence, learning, embodied systems and networks. Preliminary proposals are required by December 2, 2026, at 5 p.m. in the submitting organization’s local time. Invited full proposals are due July 27, 2027.

Communication complexity studies how cooperating parties solve computational problems while exchanging as little information as possible. This workshop addresses its core theory and applications to streaming and sublinear algorithms, data structures, distributed computation, learning, cryptography and game theory. Participants will connect methods from probability, combinatorics, graph theory, matrix analysis and information theory. Cross-disciplinary collaboration is intended to advance open problems, reveal relationships between these areas and support emerging researchers.

Investigate basal ganglia mechanisms of learning and adaptive behaviour in Jeff Wickens’s Neurobiology Research Unit, focusing on dopamine and acetylcholine dynamics in the rodent striatum. The work combines two-photon imaging, fibre photometry and behavioural tasks in virtual reality or operant boxes, with analysis and manuscript preparation. This full-time appointment initially lasts two years, starting as soon as possible after 1 January 2027. Expected annual salary is JPY 4.6–5.9 million. Applications remain open until all positions are filled. Follow the official page’s email instructions

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.

Nick Turk-Browne presents research using infant functional MRI to investigate how the hippocampus supports learning and memory early in life. The seminar connects neural development, memory formation and infantile amnesia through new methods for studying awake infants.

Daniel Levenstein discusses spontaneous activity in the hippocampus and cortex, especially during sleep, and its relationship to learning, memory and navigation. He explains how AI models can help investigate these dynamics, alongside questions about model systems, cognitive maps and the relationship between neuroscience experiments and theory.

Andrew Huberman and Fei-Fei Li discuss how artificial intelligence works, spatial intelligence, human cognition, creativity, learning, health and human agency.

Alison Barth explains how distinct neuron types contribute to cortical function and how learning can be used as an experimental window into the organization and plasticity of cortical circuits.

Nature reporters discuss the chemical signal used by naked mole-rat queens to maintain reproductive hierarchy and how people reason when learning unfamiliar games.

This lecture examines the development and evolution of insect learning centres. It asks how expanded neuronal populations and cell-type diversity support the behaviour of social species such as ants. Mechanisms in insect mushroom bodies are compared with changes in the mammalian cortex.

Tomaso Poggio discusses principles that could explain learning and intelligence in brains and machines. Topics include the relationship between engineering and theory, levels of explanation, the role of learning, and compositional sparsity and genericity as potential explanations for the effectiveness of deep networks.

Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning

Tim Vogels discusses the interaction of excitatory and inhibitory plasticity and its implications for learning and stable network activity.

Julie Sørlie Paus-Knudsen discusses learning and intelligence in bees and bumblebees alongside their ecological roles. Conversation in Norwegian.

Understanding how brains learn requires bridging evidence across scales—from behaviour and neural circuits to cells, synapses, and molecules. In our work, we use computational modelling and data analysis to explore how the physical properties of neurons and neural circuits constrain learning. These include limits imposed by brain wiring, energy availability, molecular noise, and the 3D structure of dendritic spines. In this talk I will describe one such project testing if wiring motifs from fly brain connectomes can improve performance of reservoir computers, a type of recurrent neural network

Sleep supports learning both by preparing the brain to encode information and by stabilizing memories afterward. This seminar examines the brain activity involved in sleep-dependent consolidation and how those mechanisms change across the lifespan, from childhood into older age.

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new t

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphas

Vijay Namboodiri and Ali Mohebi examine competing accounts of dopamine's role in learning. They contrast the familiar reward-prediction-error interpretation with a retrospective account centred on causes and effects, reviewing the history, behavioural evidence and controversy surrounding this reassessment of dopamine signalling.

Friedemann Zenke discusses learning in spiking networks and the challenges of translating machine-learning methods into biologically plausible rules.