Dopamine

…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

…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.

…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

…Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. Ho

…Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. Ho

Hallucinations are percepts experienced in the absence of external sensory input. Patients with hallucinations show both behavioral and neural alterations in statistical learning in simple sensory tasks. Predictive processing accounts of hallucinations suggest that these alterations bias patients’ perception toward their sensory expectations. However, 1) whether this framework holds when considering real-world hallucinations with complex content and 2) which upstream neural mechanisms cause these alterations in learning are hotly debated. To address these questions, we used fMRI to record neur

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how animals learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large arena. Combining core features of quantitative models of foraging and decision-making, we developed a novel reinforcement learning (

Neuromodulatory signals are powerful and prevalent influences on behavior. For example, dopaminergic inputs to the dorsolateral striatum are correlated with action selection and invigoration, and the loss of dopaminergic drive in Parkinson's disease manifests in deficits of movement timing and initiation. While the role of dopamine in the cortex-basal ganglia-thalamaus (C-BG-T) loop has been explored within the framework of reinforcement learning, computational models of how dopamine might influence fast-timescale movement generation have not been explored to the same extent. Here, we propose

A central goal of modern neuroscience is to relate structure, dynamics, and computation in recurrent neural networks (RNNs), in particular those that can be created via biological learning rules. To date, most work on RNN training has focused on either efficient but non-biological rules (e.g., backprop, FORCE) or biologically plausible rules with poor learning efficiency (e.g., Hebbian learning). However, some of the fastest, strongest learning rules thought to act in vivo are not Hebbian, for instance in the fly mushroom body, where plasticity depends on high-dimensional presynaptic activity

…We monitored dopamine in the tail of the striatum (TS), a region at the interface of sensory processing and action selection that receives direct auditory cortex inputs. Given mesolimbic dopamine's estab

…While the dopaminergic system is thought to broadcast a scalar reward prediction error, the serotonin system’s function has been difficult to elucidate, partly due to the heterogeneity of its individual neuronal responses [1]. Although serotonergic neurons are known to respond to various salient events [2] and rewards [3], which can be accounted for by ad-hoc extensions to a reinforcement learning framework, our study presents the first comprehensive, normative framewor

…we build on the traditional view that dopamine (DA) projections within the basal ganglia (BG) drive RBL via reinforcement learning, while the cerebellum (CB) supports EBL via supervised learning. In doing so, we introduce a novel learning framework that combines DA-driven RBL with CB-driven EBL. Specifically, we reformulate the traditional RBL and EBL synaptic weight updates in terms of a shared ‘action-gradient’ spac

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

…disease (PD), the brain loses dopaminergic neurons in the substantia nigra compacta (SNc). Highly involved in the basal ganglia-cortex-thalamus-cerebellum (BG-Ctx-T-Cb) network, the main role of dopamine (DA) is in learning, decision-making and motor control, as manifested in the PD symptoms such as rigidity, slowness of movements, tremor and poor behavioural flexibility. Following DA loss, the un

Dopamine plays a crucial role in the neural mechanisms underlying behavior, yet quantifying its impact through genetic modifications presents significant challenges, particularly due to the high-dimensional nature of the resulting data. In this study, we address these challenges by proposing a novel methodological framework using the Random Forest algorithm, tailored for robust multivariate analysis without the typical assumptions required in conventional statistical testing. Our experimental approach involved detailed video recordings of Drosophila melanogaster larvae, focusing on the effects

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.

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

Achyrocline satureoides aqueous extract (ASAE) protective effects were evaluated on dopaminergic neurodegeneration induced by reserpine in Caenorhabditis elegans (C. elegans). Strain BY200 was cultured following standard procedure. The worms (L1 stage) were exposed to the treatments, ASAE (10 or 25 mg/ml) or no for 24 hours, followed by the induction of dopaminergic damage (reserpine, 60 µM, positive control; PC) or no (negative control; NC). Worm’s images (n=20) were captured using a fluorescence microscope and quantified using the ImageJ software. The locomotor behavior (body bends) of the w

Neuromodulators like dopamine are critical regulators of associative learning. Indeed, dopaminergic neurons in the dorsal tegmental area interact with the lateral central amygdala (CEl) circuitry involved in fear behavior. Thus, fear learning modulates the microcircuit of the CEl composed of somatostatin-positive (SST+) and protein kinase C-positive (PKCδ+) neurons. During this process, dopamine 1 receptor (D1R) activation enables LTP induction in SST+ CEl neurons (Groessl et al. 2018, Nat Neurosci 21:952-962).In fact, a detailed analysis of dopaminergic cellular effects on synaptic transmissi

Adinazolam (ADZ) is one of the benzodiazepine-type new psychoactive substances (NPSs) with anxiolytic, anticonvulsant, and antidepressant effects. Although it is previously reported that high dose of ADZ administration impaired psychomotor performance and memory, abuse potential and drug dependence of ADZ have not been fully investigated. In this study, we evaluated whether ADZ has abuse potential, and lead in drug dependence and withdrawal symptoms when ADZ treatment was abruptly stopped. ADZ (0.01, 0.03, and 0.1 mg/kg/inf) increased intravenous self-administration in Sprague Dawley rats, sug

…STX1A KO, partially ameriolated with dopamine (DA) receptor agonis

…shown to critically adjust levels of dopamine in the prefrontal cortex (PFC) through astroglial vesicular monoamine transporter 2 (aVMAT2). Our recent data also indicate aVMAT2 expression in the nucleus accumbens (NAcc)., As SUD engages common neural pathways involved in natural reward processing i