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.

Enrofloxacin (ENR), a third-generation fluoroquinolone veterinary antibiotic, is widely used in aquaculture and animal husbandry for bacterial infection control due to its broad-spectrum antimicrobial activity, potent bactericidal efficacy, and cost-effectiveness. However, its excessive and non-standard use has caused widespread residues in animal-derived foods, which pose severe risks to human gastrointestinal, nervous and hepatorenal systems, induce bacterial resistance, and threaten global food safety and ecological sustainability. Herein, we developed a label-free, rapid, and highly sensitive fluorescence aptasensor for ENR detection based on G-triplex signal amplification. The ENR-specific aptamer was pre-hybridized with a guanine-rich sequence to suppress G-triplex formation and maintain a low thioflavin T (ThT) background. Upon ENR addition, target binding releases the guanine-rich strand, which folds into a stable G-triplex and strongly enhances ThT fluorescence for quantitative detection. Under optimal conditions, this aptasensor showed a linear range of 1.0 nM to 1.0 μM (R = 0.9925) with a detection limit of 1.2 nM. The assay takes only 25 min without complex instruments or chemical labeling. It exhibited excellent selectivity and satisfactory recoveries (92.36-105.78%) in spiked milk samples, providing a facile, low-cost, and reliable tool for on-site ENR residue screening in complex food matrices.

Effective cancer immunotherapy is hindered by immunosuppressive crosstalk within the tumor microenvironment. We engineered a tumor immune microenvironment-on-a-chip (TIMoC) that recapitulates the vascularized, hypoxic, and spatially organized niche of human solid liver tumors. We employed TIMoC to dissect the reciprocal interaction between macrophages and natural killer (NK) cells. Macrophages induced NK cell dysfunction, while dysfunctional NK cells promoted M2 macrophage polarization. This bidirectional impairment created a self-perpetuating immunosuppressive loop. TIMoC served as an in vitro screening tool, confirming the limited efficacy of TIGIT blockade in a multicellular context and revealing synergistic anti-tumor activity for combinations of a macrophage-reprogramming agent (resiquimod) with NK cell-targeting antibodies. By incorporating patient-derived organotypic tumor spheroids and autologous immune cells, the personalized TIMoC platform modeled patient-specific responses and evaluated effective drug combinations, demonstrating its potential to guide precision immunotherapy. This work elucidates a key immunosuppressive axis and introduces a versatile platform for rationally designing combination immunotherapies.

In the context of spiking neural networks, the temporal coding hypothesis is increasingly preferred over the rate coding hypothesis due to its advantages in processing speed and energy efficiency. In temporal coding, synaptic delays are crucial for processing signals with precise spike timings, known as spiking motifs. Synaptic delays are however bounded in the brain and can thus be shorter than the duration of a motif. This prevents the use of motif recognition methods that consist of setting heterogeneous delays to synchronize the input spikes on a single output neuron acting as a coincidence detector. To address this issue, we developed a method to detect motifs of arbitrary length using a sequence of output neurons connected to input neurons by bounded synaptic delays. Each output neuron is associated with a sub-motif of bounded duration. A motif is recognized if all sub-motifs are sequentially detected by the output neurons. We simulated this network using leaky integrate-and-fire neurons and tested it on the spiking heidelberg digits (SHD) database, that is, on audio data converted to spikes via a cochlear model, as well as on random simultaneous motifs. The results demonstrate that the network can effectively recognize motifs of arbitrary length extracted from the SHD database. Our method features a correct detection rate of about 60% in presence of ten simultaneous motifs from the SHD dataset and up to 80% for five motifs, showing the robustness of the network to noise. Results on random overlapping patterns show that the recognition of a single motif overlapping with other motifs is most effective for a large number of input neurons and sparser motifs. Our method provides a foundation for more general models for the storage and retrieval of neural information of arbitrary temporal lengths.