98%
921
2 minutes
20
Like human language, song in songbirds is learned during an early sensitive period and is facilitated by motivation to seek out social interactions with vocalizing adults. Songbirds are therefore powerful models with which to understand the neural underpinnings of vocal learning. Social motivation and early social orienting are thought to be mediated by the oxytocin system; however, the developmental trajectory of oxytocin receptors in songbirds, particularly as it relates to song learning, is currently unknown. This gap in knowledge has hindered the development of songbirds as a model of the role of social orienting in vocal learning. In this study, we used quantitative PCR to measure oxytocin receptor expression during the sensitive period of song learning in zebra finches (Taeniopygia guttata). We focused on brain regions important for social motivation, attachment, song recognition, and song learning. We detected expression in these regions in both sexes from posthatch day 5 to adulthood, encompassing the entire period of song learning. In this species, only males sing; we found that in regions implicated in song learning specifically, oxytocin receptor mRNA expression was higher in males than females. These sex differences were largest during the developmental phase when males attend to and memorize tutor song, suggesting a functional role of expression in learning. Our results show that oxytocin receptors are expressed in relevant brain regions during song learning, and thus provide a foundation for developing the zebra finch as a model for understanding the mechanisms underlying the role of social motivation in vocal development.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795483 | PMC |
http://dx.doi.org/10.1002/dneu.22851 | DOI Listing |
Sci Robot
September 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
According to productive failure (PF) theory, experiencing failure during problem-solving can enhance students' knowledge acquisition in subsequent instruction. However, challenging students with problems beyond their current capabilities may strain their skills, prior knowledge, and emotional well-being. To address this, we designed a social robot-assisted teaching activity in which students observed a robot's unsuccessful problem-solving attempts, offering a PF-like preparatory effect without requiring direct failure.
View Article and Find Full Text PDFJ Phys Chem Lett
September 2025
Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics, Central South University, Changsha, Hunan 410083, China.
The optoelectronic properties of perovskite/two-dimensional (2D) material van der Waals heterojunctions provide greater potential for innovative neuromorphic devices. However, the traditional growth of heterojunctions still relies on strict lattice matching and high-temperature processes, which hinder high-quality interface construction and efficient carrier transport. Here, the 2D CsPbI/MoS heterojunction is realized via the van der Waals epitaxy process, overcoming lattice matching limitations.
View Article and Find Full Text PDFSci Adv
September 2025
School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Developing intelligent robots with integrated sensing capabilities is critical for advanced manufacturing, medical robots, and embodied intelligence. Existing robotic sensing technologies are limited to recording of acceleration, driving torque, pressure feedback, and so on. Expanding and integrating with the multimodal sensors to mimic and even surpass the human feeling is substantially underdeveloped.
View Article and Find Full Text PDFPhys Chem Chem Phys
September 2025
State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
Selenium, as an important semiconductor material, exhibits significant potential for understanding lattice dynamics and thermoelectric applications through its thermal transport properties. Conventional empirical potentials are often unable to accurately describe the phonon transport properties of selenium crystals, which limits in-depth understanding of their thermal conduction mechanisms. To address this issue, this study developed a high-precision machine learning potential (MLP), with training datasets generated molecular dynamics simulations.
View Article and Find Full Text PDFProc Mach Learn Res
November 2024
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP. However, progress in the graph domain remains limited due to fundamental challenges represented by feature heterogeneity and structural heterogeneity. Recent efforts have been made to address feature heterogeneity via Large Language Models (LLMs) on text-attributed graphs (TAGs) by generating fixed-length text representations as node features.
View Article and Find Full Text PDF