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Perceptual decision-making tasks are essential to many fields of neuroscience. Current protocols generally reward deprived animals with water. However, balancing animals' deprivation level with their well-being is challenging, and trial number is limited by satiation. Here, we present electrical stimulation of the medial forebrain bundle (MFB) as an alternative that avoids deprivation while yielding stable motivation for thousands of trials. Using licking or lever press as a report, MFB animals learnt auditory discrimination tasks at similar speed to water-deprived mice. Moreover, they more reliably reached higher accuracy in harder tasks, performing up to 4,500 trials per session without loss of motivation. MFB stimulation did not impact the underlying sensory behavior since psychometric parameters and response times are preserved. MFB mice lacked signs of metabolic or behavioral stress compared with water-deprived mice. Overall, MFB stimulation is a highly promising tool for task learning because it enhances task performance while avoiding deprivation.
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http://dx.doi.org/10.1016/j.crmeth.2022.100355 | DOI Listing |
JMIR Mhealth Uhealth
September 2025
Department of Neurology, School of Medicine, Washington University in St. Louis, 660 South Euclid Avenue, St Louis, MO, 63130, United States, 1 9548065162.
Background: Unsupervised cognitive assessments are becoming commonly used in studies of aging and neurodegenerative diseases. As assessments are completed in everyday environments and without a proctor, there are concerns about how common distractions may impact performance and whether these distractions may differentially impact those experiencing the earliest symptoms of dementia.
Objective: We examined the impact of self-reported interruptions, testing location, and social context during testing on remote cognitive assessments in older adults.
PLoS One
September 2025
Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
Generalized visual grounding tasks, including Generalized Referring Expression Comprehension (GREC) and Segmentation (GRES), extend the classical visual grounding paradigm by accommodating multi-target and non-target scenarios. Specifically, GREC focuses on accurately identifying all referential objects at the coarse bounding box level, while GRES aims for achieve fine-grained pixel-level perception. However, existing approaches typically treat these tasks independently, overlooking the benefits of jointly training GREC and GRES to ensure consistent multi-granularity predictions and streamline the overall process.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Significant progress has been made in applying deep learning for the automatic diagnosis of skin lesions. However, most models remain unexplainable, which severely hinders their application in clinical settings. Concept-based ante-hoc interpretable models have the potential to clarify the decision-making process of diagnosis by learning high-level, human-understandable concepts, while they can only provide numerical values of conceptual contributions.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The Segment Anything Model (SAM) has attracted considerable attention due to its impressive performance and demonstrates potential in medical image segmentation. Compared to SAM's native point and bounding box prompts, text prompts offer a simpler and more efficient alternative in the medical field, yet this approach remains relatively underexplored. In this paper, we propose a SAM-based framework that integrates a pre-trained vision-language model to generate referring prompts, with SAM handling the segmentation task.
View Article and Find Full Text PDF