Conjunctive Processing of Locomotor Signals by the Ventral Tegmental Area Neuronal Population

The ventral tegmental area (VTA) plays an essential role in reward and motivation. How the dopamine (DA) and non-DA neurons in the VTA engage in motivation-based locomotor behaviors is not well understood. We recorded activity of putative DA and non-DA neurons simultaneously in the VTA of awake mice engaged in motivated voluntary movements such as wheel running. Our results revealed that VTA non-DA neurons exhibited significant rhythmic activity that was correlated with the animal's running rhythms. Activity of putative DA neurons also correlated with the movement behavior, but to a lesser degree. More importantly, putative DA neurons exhibited significant burst activation at both onset and offset of voluntary movements. These findings suggest that VTA DA and non-DA neurons conjunctively process locomotor-related motivational signals that are associated with movement initiation, maintenance and termination

Striatal Dopamine Transmission Is Subtly Modified in Human A53Tα-Synuclein Overexpressing Mice

Mutations in, or elevated dosage of, SNCA, the gene for α-synuclein (α-syn), cause familial Parkinson's disease (PD). Mouse lines overexpressing the mutant human A53Tα-syn may represent a model of early PD. They display progressive motor deficits, abnormal cellular accumulation of α-syn, and deficits in dopamine-dependent corticostriatal plasticity, which, in the absence of overt nigrostriatal degeneration, suggest there are age-related deficits in striatal dopamine (DA) signalling. In addition A53Tα-syn overexpression in cultured rodent neurons has been reported to inhibit transmitter release. Therefore here we have characterized for the first time DA release in the striatum of mice overexpressing human A53Tα-syn, and explored whether A53Tα-syn overexpression causes deficits in the release of DA. We used fast-scan cyclic voltammetry to detect DA release at carbon-fibre microelectrodes in acute striatal slices from two different lines of A53Tα-syn-overexpressing mice, at up to 24 months. In A53Tα-syn overexpressors, mean DA release evoked by a single stimulus pulse was not different from wild-types, in either dorsal striatum or nucleus accumbens. However the frequency responsiveness of DA release was slightly modified in A53Tα-syn overexpressors, and in particular showed slight deficiency when the confounding effects of striatal ACh acting at presynaptic nicotinic receptors (nAChRs) were antagonized. The re-release of DA was unmodified after single-pulse stimuli, but after prolonged stimulation trains, A53Tα-syn overexpressors showed enhanced recovery of DA release at old age, in keeping with elevated striatal DA content. In summary, A53Tα-syn overexpression in mice causes subtle changes in the regulation of DA release in the striatum. While modest, these modifications may indicate or contribute to striatal dysfunction

Convergent Processing of Both Positive and Negative Motivational Signals by the VTA Dopamine Neuronal Populations

Dopamine neurons in the ventral tegmental area (VTA) have been traditionally studied for their roles in reward-related motivation or drug addiction. Here we study how the VTA dopamine neuron population may process fearful and negative experiences as well as reward information in freely behaving mice. Using multi-tetrode recording, we find that up to 89% of the putative dopamine neurons in the VTA exhibit significant activation in response to the conditioned tone that predict food reward, while the same dopamine neuron population also respond to the fearful experiences such as free fall and shake events. The majority of these VTA putative dopamine neurons exhibit suppression and offset-rebound excitation, whereas ∼25% of the recorded putative dopamine neurons show excitation by the fearful events. Importantly, VTA putative dopamine neurons exhibit parametric encoding properties: their firing change durations are proportional to the fearful event durations. In addition, we demonstrate that the contextual information is crucial for these neurons to respectively elicit positive or negative motivational responses by the same conditioned tone. Taken together, our findings suggest that VTA dopamine neurons may employ the convergent encoding strategy for processing both positive and negative experiences, intimately integrating with cues and environmental context

Age-dependent effects of protein restriction on dopamine release

FUNDING AND DISCLOSURE This work was supported by the Biotechnology and Biological Sciences Research Council [grant # BB/M007391/1 to J.E.M.], the European Commission [grant # GA 631404 to J.E.M.], The Leverhulme Trust [grant # RPG-2017-417 to J.E.M.] and the Tromsø Research Foundation [grant # 19-SG-JMcC to J. E. M.). The authors declare no conflict of interest. ACKNOWLEDGEMENTS The authors would like to acknowledge the help and support from the staff of the Division of Biomedical Services, Preclinical Research Facility, University of Leicester, for technical support and the care of experimental animals.Peer reviewedPublisher PD

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards