Topic: dopaminergic neurons

The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference (TD) reinforcement learning. The TD framework predicts that some neuronal elements should represent the reward prediction error (RPE), which means they signal the difference between the expected future rewards and the actual rewards. The prominence of the TD theory arises from the observation that firing properties of dopaminergic neurons in the ventral tegmental area appear similar to those of RPE model-neurons in TD learning. Previous implementations of TD learning assume a fixed temp

Quantification of animal behavior is critical to neuroscience, genetics, and ethology research. Despite the recent development of a deep-learning-based pose estimation model, the usage of those systems has been limited due to the various video recording environment and hardware systems. Here we introduce a novel open-field experimental hardware with multi-vision and a 3D real-time behavior analysis system, based on the efficient and rapid object-detection deep learning algorithm, named AVATAR (AI Vision Analysis for Three-dimensional Action in Real-time). Combining this system with an automate

Conventionally, neurons are thought to be cellular units that process synaptic inputs into synaptic spikes. However, it is well known that neurons can also spike spontaneously and display a rich repertoire of firing properties with no apparent functional relevance e.g. in in vitro cortical slice preparations. In this talk, I will propose a hypothesis according to which intrinsic excitability in neurons may be a survival mechanism to minimize toxic byproducts of the cell’s energy metabolism. In neurons, this toxicity can arise when mitochondrial ATP production stalls due to limited ADP. Under t

Can alterations in experience trigger different plastic modifications in neuronal structure and function, and if so, how do they integrate at the cellular level? To address this question, we interrogated circuitry in the mouse olfactory bulb responsible for the earliest steps in odor processing. We induced experience-dependent plasticity in mice of either sex by blocking one nostril for one day, a minimally invasive manipulation that leaves the sensory organ undamaged and is akin to the natural transient blockage suffered during common mild rhinal infections. We found that such brief sensory d

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

Parkinson's disease (PD) is the second most present neurodegenerative disease in the world after Alzheimer's. It is due to the progressive and irreversible loss of dopaminergic neurons of the substantia nigra Pars Compacta. Alpha synuclein deposits and the appearance of Lewi bodies are systematically associated with it. PD is characterized by four cardinal motor symptoms: bradykinesia / akinesia, rigidity, postural instability and tremors at rest. These symptoms appear when 80% of the dopaminergic endings disappear in the striatum. According to Braak's theory, non-motor symptoms appear much ea

Parkinson’s disease is affects millions of people around the world. The disease is characterized by typical movement defects that are caused by the loss of dopaminergic neurons, but several very debilitating non-motor symptoms occur more than 10 years before the motor symptoms. I will discuss how we study these non-motor symptoms including sleep disturbances and olfactory defects using large collections of knock in fruit flies that model the numerous familial forms of Parkinson’s disease as well as using human iPS cells from patients. A common emerging theme are defects in protein homeostasis

Brain activity lies on a very complex network. This complexity is multiple but a prominent one is the non-linear and multi-factorial interaction between its nodes. In the specific case of Parkinson's 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

Parkinson’s Disease (PD) is a neurodegenerative disorder associated with the loss of dopaminergic neurons in the nigrostriatal circuit, and the development of motor and non-motor symptoms. Currently, no disease-modifying pharmacological treatments are available, and dopamine-based therapies help to mitigate symptoms but also cause side-effects. In this context, indirect evidence has suggested that activation of metabotropic Glutamate Receptor 3 (mGluR3) exerts neuroprotective effects in animal models of PD. However, the lack of selective agonists/ligands for this receptor has hindered more in-