Striatal dopamine plays key roles in our normal and pathological goal-directed actions. To understand dopamine function, much attention has focused on how midbrain dopamine neurons modulate their firing patterns. However, we identify a presynaptic mechanism that triggers dopamine release directly, bypassing activity in dopamine neurons. We paired electrophysiological recordings of striatal channelrhodopsin2-expressing cholinergic interneurons with simultaneous detection of dopamine release at carbon-fiber microelectrodes in striatal slices. We reveal that activation of cholinergic interneurons by light flashes that cause only single action potentials in neurons from a small population triggers dopamine release via activation of nicotinic receptors on dopamine axons. This event overrides ascending activity from dopamine neurons and, furthermore, is reproduced by activating ChR2-expressing thalamostriatal inputs, which synchronize cholinergic interneurons in vivo. These findings indicate that synchronized activity in cholinergic interneurons directly generates striatal dopamine signals whose functions will extend beyond those encoded by dopamine neuron activity.

In this issue of Neuron, Threlfell et al. (2012) report that synchronous activation of cholinergic interneurons evokes striatal dopamine release by activating presynaptic nicotinic acetylcholine receptors. These findings call for a fundamental reevaluation of the long-standing view that dopamine and acetylcholine "feud" over control of striatal circuitry.

The present investigation had two aims: (1) to study responses of dopamine neurons to stimuli with attentional and motivational significance during several steps of learning a behavioral task, and (2) to study the activity of dopamine neurons during the performance of cognitive tasks known to be impaired after lesions of these neurons. Monkeys that had previously learned a simple reaction time task were trained to perform a spatial delayed response task via two intermediate tasks. During the learning of each new task, a total of 25% of 76 dopamine neurons showed phasic responses to the delivery of primary liquid reward, whereas only 9% of 163 neurons responded to this event once task performance was established. This produced an average population response during but not after learning of each task. Reward responses during learning were significantly more numerous and pronounced in area A10, as compared to areas A8 and A9. Dopamine neurons also showed phasic responses to the two conditioned stimuli. These were the instruction cue, which was the first stimulus in each trial and indicated the target of the upcoming arm movement (58% of 76 neurons during and 44% of 163 neurons after learning), and the trigger stimulus, which was a conditioned incentive stimulus predicting reward and eliciting a saccadic eye movement and an arm reaching movement (38% of neurons during and 40% after learning). None of the dopamine neurons showed sustained activity in the delay between the instruction and trigger stimuli that would resemble the activity of neurons in dopamine terminal areas, such as the striatum and frontal cortex. Thus, dopamine neurons respond phasically to alerting external stimuli with behavioral significance whose detection is crucial for learning and performing delayed response tasks. The lack of sustained activity suggests that dopamine neurons do not encode representational processes, such as working memory, expectation of external stimuli or reward, or preparation of movement. Rather, dopamine neurons are involved with transient changes of impulse activity in basic attentional and motivational processes underlying learning and cognitive behavior.

Basal Ganglia Advances is a collection highlighting research on the structure, function, and disorders of the basal ganglia. It features studies spanning neuroscience, clinical insights, and computational models, serving as a hub for advances in movement, cognition, and behavior.

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.

Histological image classification plays a critical role in biomedical research and diagnostic processes. Advances in the field of deep learning present significant opportunities for enhancing diagnostic accuracy and developing automated decision support systems. This study aims to comparatively evaluate the out-of-distribution generalization and cross-domain classification performance of different deep neural network encoders. In this study, models were trained on an internal dataset of 4307 hematoxylin and eosin (H&E) stained images of male rat lung, cerebellum, and adipose tissues, obtained under ethical committee approval (Ege University, HADYEK 2026-06). To evaluate genuine generalization, testing was conducted on a separate, diverse external cohort of 600 mixed human-and-animal images. Nine different deep learning architectures-MobileNetV3-S, MobileNetV3-L, DenseNet121, DenseNet201, ConvNeXt-S, ConvNeXt-B, DINOv3 ViT-S, DINOv3 ViT-H+, and the pathology foundation model UNI2-h-were evaluated using frozen feature extraction combined with a linear probe. For performance evaluation, accuracy, sensitivity, specificity, F1 score, Cohen's κ, and ROC-AUC metrics were analyzed along with per-image inference times. While all models achieved near-perfect results during internal cross-validation, their performance diverged significantly on the external dataset, confirming that the observed differences reflect genuine cross-domain generalization capabilities rather than under-fitting. All competitive models demonstrated high performance in classifying adipose and cerebellum tissues, achieving an F1 score of 95% or higher for these tissues. In distinguishing the lung tissue, which has the most diverse structure and is the most difficult to classify, the UNI2-h model emerged as the most successful, achieving an F1 score of 97.4% and a recall of 95.0%. When evaluated in terms of computational efficiency, the MobileNetV3-Small model stood out as having the lowest processing time among all scenarios, demonstrating an inference time of 9.4 ms on the CPU and 12.5 ms on the GPU. In conclusion, while the pathology foundation model UNI2-h achieved the highest accuracy values across all metrics, the ConvNeXt-Small model was identified as the system providing the optimal balance between speed and accuracy. These findings indicate that although UNI2-h stands out for the most precise results in cross-domain classifications, the ConvNeXt-Small architecture emerges as an ideal alternative, particularly in practical applications where computational efficiency is critical and resources are constrained.

To compare the marginal and internal adaptation and to evaluate the clinical performance and patient satisfaction of CAD/CAM-fabricated all-ceramic crowns produced from lithium disilicate (LDS) and lithium disilicate-reinforced lithium aluminosilicate (LAS) over a 24-month follow-up period.