Precise coordination across hemispheres is a critical feature of many complex motor circuits. In the avian song system the robust nucleus of the arcopallium (RA) plays a key role in such coordination. It is simultaneously the major output structure for the descending vocal motor pathway, and it also sends inputs to structures in the brain stem and thalamus that project bilaterally back to the forebrain. Because all birds lack a corpus callosum and the anterior commissure does not interconnect any of the song control nuclei directly, these bottom-up connections form the only pathway that can coordinate activity across hemispheres. In this study, we show that unilateral lesions of RA in adult male zebra finches (Taeniopigia guttata) completely and permanently disrupt the bird's stereotyped song. In contrast, lesions of RA in juvenile birds do not prevent the acquisition of normal song as adults. These results highlight the importance of hemispheric interdependence once the circuit is established but show that one hemisphere is sufficient for complex vocal behavior if this interdependence is prevented during a critical period of development. The ability of birds to sing with a single RA provides the opportunity to test the effect of targeted microlesions in RA without confound of functional compensation from the contralateral RA. We show that microlesions cause significant changes in song temporal structure and implicate RA as playing a major part in the generation of song temporal patterns. These findings implicate a dual role for RA, first as part of the program generator for song and second as part of the circuit that mediates interhemispheric coordination.
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http://dx.doi.org/10.1152/jn.00830.2007 | DOI Listing |
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
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