Topic: dopamine

Jérémie Naudé and Albert Moukheiber join Alexandra Delbot to examine popular misconceptions about the brain, including claims about dopamine detoxes. They discuss what neuroscience experiments establish about dopamine and reward prediction, and how simplified biological narratives can distort research and public understanding.

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

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.

Masud Husain explains the neuroscience of motivation, dopamine, effort, and reward, together with research on apathy, dementia, Parkinson's disease, Alzheimer's disease, and cognitive resilience.

This talk aims to answer these questions along four dimensions. First is history. RL was the basis of AI long before the term AI was introduced in 1956. The first machine learning (ML) systems were based on RL even before digital computers existed. Despite notable early successes of ML based on RL, RL essentially disappeared from ML until relatively recently. A second reason for renewed interest in RL is the clarification of some misunderstandings that have been prevalent in the ML community. A third, and most important, reason for this resurgence is that new, or rediscovered, algorithms and

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. Ho

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. Ho

Functional connectivity has been the focus of many research groups aiming to study the interaction between cells and brain regions. A standard method for analyzing connectivity is to statistically compare pairwise interactions between cells or brain regions across behavioral states or conditions. This methodology ignores the intrinsic properties of functional connectivity as a multivariate and dynamic signal, expressing the correlational configuration of the network. In this talk, I will present a geometric approach, combining Graph Theory and Riemannian Geometry to build "a graph of graphs" a

Wearable sensors that detect and quantify biomarkers in retrievable biofluids (e.g., interstitial fluid, sweat, tears) provide information on human dynamic physiological and psychological states. This information can transform health and wellness by providing actionable feedback. Due to outdated and insufficiently sensitive technologies, current on-body sensing systems have capabilities limited to pH, and a few high-concentration electrolytes, metabolites, and nutrients. As such, wearable sensing systems cannot detect key low-concentration biomarkers indicative of stress, inflammation, metabo

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

Hallucinations are a core symptom of psychotic disorders and have traditionally been difficult to study biologically. We developed a new behavioral computational approach to measure hallucinations-like perception in humans and mice alike. Using targeted neural circuit manipulations, we identified a causal role for striatal dopamine in mediating hallucination-like perception. Building on this, we currently investigate the neural and immunological upstream regulators of these dopaminergic circuits with the goal to identify new biological treatment targets for psychosis

Schizophrenia is a neuropsychiatric disorder characterized by positive symptoms (such as hallucinations and delusions), negative symptoms (such as avolition and withdrawal) and cognitive dysfunction1. Schizophrenia is highly heritable, and genetic studies are playing a pivotal role in identifying potential biomarkers and causal disease mechanisms with the hope of informing new treatments. Genome-wide association studies (GWAS) identified nearly 270 loci with a high statistical association with schizophrenia risk; however each locus confers only a small increase in risk therefore it is difficul

The human brain faces a variety of computational dilemmas, including the flexibility/stability, the speed/accuracy and the labor/leisure tradeoff. I will argue that striatal dopamine is particularly well suited to dynamically regulate these computational tradeoffs depending on constantly changing task demands. This working hypothesis is grounded in evidence from recent studies on learning, motivation and cognitive control in human volunteers, using chemical PET, psychopharmacology, and/or fMRI. These studies also begin to elucidate the mechanisms underlying the huge variability in catecholamin

The basal ganglia are a collection of brain areas that are connected by a variety of synaptic pathways and are a site of significant reward-related dopamine release. These properties suggest a possible role for the basal ganglia in action selection, guided by reinforcement learning. In this talk, I will discuss a framework for how this function might be performed and computational results using an upward mapping to identify putative low-dimensional control ensembles that may be involved in tuning decision policy. I will also present some recent experimental results and theory – related to ef

Dopamine is a neuromodulator that codes information on various time scales. I will discuss recent progress on the identification of fast release mechanisms for dopamine in the mouse striatum. I will present data on triggering mechanisms of dopamine release and evaluate its roles in striatal regulation. In the long-term, our work will allow for a better understanding of the mechanisms and time scales of dopamine coding in health and disease.

Inter-individual variability refers to differences in the expression of behaviors between members of a population. For instance, some individuals take greater risks, are more attracted to immediate gains or are more susceptible to drugs of abuse than others. To probe the neural bases of inter-individual variability we study reward seeking and decision-making in mice, and dissect the specific role of dopamine in the modulation of these behaviors. Using a spatial version of the multi-armed bandit task, in which mice are faced with consecutive binary choices, we could link modifications of mid

Many proposed neural circuits for learning are theoretically well-grounded but have limited biological evidence to support them. Emerging anatomical and functional data from neurons in the fruit fly brain allows us to identify interconnections in the mushroom body neuropil that form an 'incentive circuit', supporting rich dynamics of memory acquisition, consolidation and forgetting. In addition, new insights into dopamine function in the fruit fly brain motivate a novel 'dopaminergic plasticity rule' that alters the weights between input sensory patterns and output neurons of the mushroom body

Human movement disorders and pharmacological studies have long suggested molecular dopamine modulates the pace of the internal clock. But how does the endogenous dopaminergic system influence the timing of our movements? We examined the relationship between dopaminergic signaling and the timing of reward-related, self-timed movements in mice. Animals were trained to initiate licking after a self-timed interval following a start cue; reward was delivered if the animal’s first lick fell within a rewarded window (3.3-7 s). The first-lick timing distributions exhibited the scalar property, and we

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that depend upon evaluation of errors in performance and reward prediction errors, respectively. In mammals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction errors; but there has been little consideration of how direct policy learning might inform our understanding. I’ll discuss our re

Transient changes in dopamine activity in response to reward and punishment have been known to regulate reward-related learning. However, the cellular basis that detects the transient dopamine signaling has long been unclear. Using two-photon microscopy and optogenetics, I have shown that transient increases and decreases of dopamine modulate plasticity of dopamine D1 and D2 receptor-expressing cells in the nucleus accumbens, respectively. At the behavioral level, I characterized that these D1 and D2 cells cooperatively tune learning by generalization and discrimination learning. Interestingly

Dr. Looger will discuss reagents for tracking Ca2+, membrane potential ("voltage"), glutamate, GABA, acetylcholine, serotonin, dopamine, etc. He will also cover optogenetics tools and methods for correlative light/electron microscopy. They make all tools freely available to everyone and work to get them in the hands of people that have limited resources.

Psychotic disorders are devastating conditions without any mechanistic treatment available. One major hurdle in the biological study of psychosis is the challenge of rigorously probing this condition in pre-clinical animal models. The goal of our research is to develop and exploit innovative frameworks for the study of psychosis in mice. In our present work, where we developed a cross-species computational psychiatry approach to probe hallucination-like perception. This enabled us to directly relate human and mouse behavior, and to demonstrate and dissect the causal role of striatal dopamine i

Drosophila can learn to associate odours with reward or punishment and the resulting memories direct odour-specific approach or avoidance behaviours. Recent progress has revealed a straightforward model for learning in which reinforcing dopaminergic neurons assign valence to odour representations in the neural ensemble of the mushroom bodies. Dopamine directed synaptic depression alters the route of odour-driven activity through the mushroom body output network. This circuit configuration and influence of internal state guide the expression of appropriate behaviour. Importantly, learned behavi

Cognitive bias is a "subjective reality" that is uniquely created in the brain and affects our various behaviors. It may lead to what is widely called irrationality in behavioral economics, such as inaccurate judgment and illogical interpretation, but it also has an adaptive aspect in terms of mental hygiene. When such cognitive bias is regarded as a product of information processing in the brain, the approach to clarify the mechanism in the brain will play a part in finding the direct relations between the brain and the mind. In my talk, I will introduce our studies investigating the neural a