Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.

The major pathological feature of Parkinson 's disease (PD), the second most common neurodegenerative disease and most common movement disorder, is the predominant degeneration of dopaminergic neurons in the substantia nigra, a part of the midbrain. Despite decades of research, the molecular mechanisms of the origin of the disease remain unknown. While the disease was initially viewed as a purely neuronal disorder, results from single-cell transcriptomics have suggested that oligodendrocytes may play an important role in the early stages of Parkinson's. Although these findings are of high relevance, particularly to the search for effective disease-modifying therapies, the actual functional role of oligodendrocytes in Parkinson's disease remains highly speculative and requires a concerted scientific effort to be better understood. This Unsolved Mystery discusses the limited understanding of oligodendrocytes in PD, highlighting unresolved questions regarding functional changes in oligodendroglia, the role of myelin in nigral dopaminergic neurons, the impact of the toxic environment, and the aggregation of alpha-synuclein within oligodendrocytes.

Understanding how corticostriatal circuits mediate behavioral selection and initiation in a naturalistic setting is critical to understanding behavior choice and execution in unconstrained situations. The central striatum (CS) is well poised to play an important role in these spontaneous processes. Using fiber photometry and optogenetics, we identify a role for CS in grooming initiation. However, CS-evoked movements resemble short grooming fragments, suggesting additional input is required to appropriately sustain behavior once initiated. Consistent with this idea, the anterior lateral motor area (ALM) demonstrates a slow ramp in activity that peaks at grooming termination, supporting a potential role for ALM in encoding grooming bout length. Furthermore, optogenetic stimulation of ALM-CS terminals generates sustained grooming responses. Finally, dual-region photometry indicates that CS activation precedes ALM during grooming. Taken together, these data support a model in which CS is involved in grooming initiation, while ALM may encode grooming bout length.

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Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

Background Neuroblastoma is a malignant childhood cancer with significant inter- and intrapatient heterogeneity arising from the abnormal differentiation of neural crest cells into sympathetic neurons. The lack of actionable mutations limits therapeutic options, highlighting the need to better understand the molecular mechanisms that drive this differentiation. Although RNA velocity has provided some insights, modeling regulatory relationships is limited. Methods To address this, we applied our integrated gene regulatory network (GRNs) inference (CARDAMOM) and simulation (HARISSA) tools using a published single-cell RNAseq dataset from human sympathoadrenal differentiation. Results Our analysis identified a 97-gene GRN that drives the transition from Schwann cell precursors to chromaffin cells and sympathoblasts, highlighting dynamic interactions such as self-reinforcing loops and toggle switches. The simulation of that GRN was able to reproduce very satisfactorily the experimentally observed gene expression distributions. Conclusions Altogether, these findings demonstrate the utility of our GRN model framework for inferring GRN structure, even in the absence of a time-resolved dataset.

*We conducted research to predict daily transpiration in crops by utilizing a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. *Data were collected from hundreds of plant specimens growing in two semi-controlled greenhouses over seven years, automatically measuring key physiological traits (serves as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision tree, Random Forest, XGBoost, and Neural Network models on this dataset to predict daily transpiration. *The Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R2 values of 0.89 on the test set (cross-validation) and R2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factors affecting transpiration. *Our results emphasize the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration.