Behavioral variability serves an essential role in motor learning by enabling sensory feedback to select those motor patterns that minimize error. Birds use auditory feedback to learn how to sing, and their songs lose variability and become highly stereotyped, or crystallized, at the end of a sensitive period for sensorimotor learning. The molecular cues that regulate song variability are not well understood. In other systems, neurotrophins, and brain-derived neurotrophic factor (BDNF) in particular, can mediate various forms of neural plasticity, including sensitive period neural circuit plasticity and activity-dependent synapse formation, and may also influence learning and memory. Here, we have tested the hypothesis that neurotrophin expression in the robust nucleus of the arcopallium (RA), the telencephalic output controlling song, regulates song variability. BDNF and its receptor trkB are expressed in RA, and BDNF expression in RA appears to be highest in juveniles, when song is most variable and plastic, and synapse density highest. Thus, song variability and synaptic connectivity could be enhanced by augmented expression of BDNF in RA. In support of this idea, we found that BDNF injections into the adult RA induced the re-expression of juvenile-like phenotypes, including song variability and an increased synaptic density in RA. Furthermore, BDNF treatment also induced vocal plasticity, characterized by syllable deletions and persistent changes to the song patterns. These results suggest that endogenous BDNF could be a molecular regulator of the song variability essential to vocal plasticity and, ultimately, to song learning.
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http://dx.doi.org/10.1002/neu.20109 | 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.
View Article and Find Full Text PDFSci Rep
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.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
Front Med (Lausanne)
January 2025
Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Background: Rhabdomyolysis (RM) frequently gives rise to diverse complications, ultimately leading to an unfavorable prognosis for patients. Consequently, there is a pressing need for early prediction of survival rates among RM patients, yet reliable and effective predictive models are currently scarce.
Methods: All data utilized in this study were sourced from the MIMIC-IV database.
Front Neurosci
January 2025
Department of Mathematics, University of Antwerp-Interuniversity Microelectronics Centre (imec), Antwerp, Belgium.
Introduction: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.
Methods: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks.
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