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

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.

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

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.

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

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

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

Psychology and neuroscience are increasingly looking to fine-grained analyses of decision-making behaviour, seeking to characterise not just the variation between subjects but also a subject's variability across time. When analysing the behaviour of each subject in a choice task, we ideally want to know not only when the subject has learnt the correct choice rule but also what the subject tried while learning. I introduce a simple but effective Bayesian approach to inferring the probability of different choice strategies at trial resolution. This can be used both for inferring when subjects le

People largely differ with respect to how well they can learn, memorize, and perceive faces. In this talk, I address two potential sources of variation. One factor might be people’s ability to adapt their perception to the kind of faces they are currently exposed to. For instance, some studies report that those who show larger adaptation effects are also better at performing face learning and memory tasks. Another factor might be people’s sensitivity to perceive fine differences between similar-looking faces. In fact, one study shows that the brain of good performers in a face memory task show

An understanding of the self helps explain not only human thoughts, feelings, attitudes but also many aspects of everyday behaviour. This talk focuses on a viewpoint - self as processes. This viewpoint emphasizes the dynamics of the self that best connects with the development of the self over time and its realist orientation. We are combining psychological experiments and data mining to comprehend the stability and adaptability of the self across various populations. In this talk, I draw on evidence from experimental psychology, cognitive neuroscience, and machine learning approaches to demon

The last decade has seen a burgeoning interest in studying the neural and computational mechanisms that underpin social learning (learning from others). Many findings support the view that learning from other people is underpinned by the same, ‘domain-general’, mechanisms underpinning learning from non-social stimuli. Despite this, the idea that humans possess social-specific learning mechanisms - adaptive specializations moulded by natural selection to cope with the pressures of group living - persists. In this talk I explore the persistence of this idea. First, I present dissociations betwee

Over the last decade, research on curiosity – the desire to seek new information – has been rapidly growing. Several studies have shown that curiosity elicits activity within the dopaminergic circuit and thereby enhances hippocampus-dependent learning. However, given this new field of research, we do not have a good understanding yet of (i) how curiosity-based learning changes across the lifespan, (ii) why some people show better learning improvements due to curiosity than others, and (iii) whether lab-based research on curiosity translates to how curiosity affects information seeking in real

Attractors play a key role in a wide range of processes including learning, memory, decision making and navigation. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking. In vitro cultured cortical neurons have been used extensively as a realistic experimental tool to understand the underlying mechanisms in neuronal assemblies. One of the main characteristics of the activity of such networks are the spontaneous synchro

In cognitive development, learning more than the input provides is a central challenge. This challenge is especially evident in learning the meaning of numbers. Integers – and the quantities they denote – are potentially infinite, as are the fractional values between every integer. Yet children’s experiences of numbers are necessarily finite. Analogy is a powerful learning mechanism for children to learn novel, abstract concepts from only limited input. However, retrieving proper analogy requires cognitive supports. In this talk, I seek to propose and examine number lines as a mathematical sch

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labeling action potentials emitted at a particularly high frequency with a metonym – bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. In this talk, I will discuss the implications of seeing the neural code as having three syllables: silences, spikes and bursts. In particular, I will describe recent theoretical

Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s hom