The neural representation of meaningful stimulus features is thought to rely on precise discharge characteristics of the auditory cortex. Precisely timed onset spikes putatively carry the majority of stimulus-related information in auditory cortical neurons but make a small contribution to stimulus representation in the auditory midbrain. Because these conclusions derive primarily from anesthetized preparations, we reexamined temporal coding properties of single neurons in the awake gerbil inferior colliculus (IC) and compared them with primary auditory cortex (AI). Surprisingly, AI neurons displayed a reduction of temporal precision compared with those in the IC. Furthermore, this hierarchical transition from high to low temporal fidelity was observed for both static and dynamic stimuli. Because most of the data that support temporal precision were obtained under anesthesia, we also reexamined response properties of IC and AI neurons under these conditions. Our results show that anesthesia has profound effects on the trial-to-trial variability and reliability of discharge and significantly improves the temporal precision of AI neurons to both tones and amplitude-modulated stimuli. In contrast, IC temporal properties are only mildly affected by anesthesia. These results underscore the pitfalls of using anesthetized preparations to study temporal coding. Our findings in awake animals reveal that AI neurons combine faster adaptation kinetics and a longer temporal window than evident in IC to represent ongoing acoustic stimuli.
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http://dx.doi.org/10.1523/JNEUROSCI.4848-06.2007 | DOI Listing |
PeerJ
January 2025
School of Biological, Environmental, and Earth Sciences, University of Southern Mississippi, Hattiesburg, MS, United States.
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MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France.
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View Article and Find Full Text PDFBMC Public Health
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Department of Statistics, University of South Africa, c/o Christiaan de Wet Road & Pioneer Avenue, Private Bag X6, Florida, 1710, Johannesburg, South Africa.
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Sci Rep
January 2025
School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
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Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore, 756089, Odisha, India.
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