Communication between auditory and vocal motor nuclei is essential for vocal learning. In songbirds, the nucleus interfacialis of the nidopallium (NIf) is part of a sensorimotor loop, along with auditory nucleus avalanche (Av) and song system nucleus HVC, that links the auditory and song systems. Most of the auditory information comes through this sensorimotor loop, with the projection from NIf to HVC representing the largest single source of auditory information to the song system. In addition to providing the majority of HVC's auditory input, NIf is also the primary driver of spontaneous activity and premotor-like bursting during sleep in HVC. Like HVC and RA, two nuclei critical for song learning and production, NIf exhibits behavioral-state dependent auditory responses and strong motor bursts that precede song output. NIf also exhibits extended periods of fast gamma oscillations following vocal production. Based on the converging evidence from studies of physiology and functional connectivity it would be reasonable to expect NIf to play an important role in the learning, maintenance, and production of song. Surprisingly, however, lesions of NIf in adult zebra finches have no effect on song production or maintenance. Only the plastic song produced by juvenile zebra finches during the sensorimotor phase of song learning is affected by NIf lesions. In this review, we carefully examine what is known about NIf at the anatomical, physiological, and behavioral levels. We reexamine conclusions drawn from previous studies in the light of our current understanding of the song system, and establish what can be said with certainty about NIf's involvement in song learning, maintenance, and production. Finally, we review recent theories of song learning integrating possible roles for NIf within these frameworks and suggest possible parallels between NIf and sensorimotor areas that form part of the neural circuitry for speech processing in humans.
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http://dx.doi.org/10.1016/j.jphysparis.2013.04.001 | DOI Listing |
Sci Rep
January 2025
Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA, USA.
Auditory perception requires categorizing sound sequences, such as speech or music, into classes, such as syllables or notes. Auditory categorization depends not only on the acoustic waveform, but also on variability and uncertainty in how the listener perceives the sound - including sensory and stimulus uncertainty, the listener's estimated relevance of the particular sound to the task, and their ability to learn the past statistics of the acoustic environment. Whereas these factors have been studied in isolation, whether and how these factors interact to shape categorization remains unknown.
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January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
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January 2025
Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute & Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P. R. China.
The unsatisfactory ionic conductivity of solid polymer electrolytes hinders their practical use as substitutes for liquid electrolytes to address safety concerns. Although various plasticizers have been introduced to improve lithium-ion conduction kinetics, the lack of microenvironment understanding impedes the rational design of high-performance polymer electrolytes. Here, we design a class of Hofmann complexes that offer continuous two-dimensional lithium-ion conduction channels with functional ligands, creating highly conductive electrolytes.
View Article and Find Full Text PDFJ Theor Biol
January 2025
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaan Xi, 710049, PR China. Electronic address:
There are evidence showing that meteorological factors, such as temperature and humidity, have critical effects on transmission of some infectious diseases, while quantifying the influence is challenging. In this study we develop a learning-explaining framework to discover the particular dependence of transmission mechanisms on meteorological factors based on multiple source data. The incidence rate based on the epidemic data and epidemic model is theoretically identified, and meanwhile the practical discovery of particular formula is feasible through deep neural networks (DNN), symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy).
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January 2025
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address:
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network design. We introduce TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography.
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