期刊
JOURNAL OF NEUROPHYSIOLOGY
卷 109, 期 7, 页码 1912-1927出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00483.2012
关键词
auditory cortex; communication; modeling; ultrasonic vocalizations
资金
- Burroughs Wellcome Career at the Scientific Interface award
- Pennsylvania Lions Hearing Research grant
- Klingenstein Award for the Neurosciences
- University of Pennsylvania Center for Collaborative Neuroscience Pilot Grant
- National Institutes of Health Computational Neuroscience Training Grant
- National Science Foundation Integrative Graduate Education and Research Traineeship (IGERT) Training Grant in Complex Scenes and Perception
Carruthers IM, Natan RG, Geffen MN. Encoding of ultrasonic vocalizations in the auditory cortex. J Neurophysiol 109: 1912-1927, 2013. First published January 16, 2013; doi:10.1152/jn.00483.2012.-One of the central tasks of the mammalian auditory system is to represent information about acoustic communicative signals, such as vocalizations. However, the neuronal computations underlying vocalization encoding in the central auditory system are poorly understood. To learn how the rat auditory cortex encodes information about conspecific vocalizations, we presented a library of natural and temporally transformed ultrasonic vocalizations (USVs) to awake rats while recording neural activity in the primary auditory cortex (A1) with chronically implanted multielectrode probes. Many neurons reliably and selectively responded to USVs. The response strength to USVs correlated strongly with the response strength to frequency-modulated (FM) sweeps and the FM rate tuning index, suggesting that related mechanisms generate responses to USVs as to FM sweeps. The response strength further correlated with the neuron's best frequency, with the strongest responses produced by neurons whose best frequency was in the ultrasonic frequency range. For responses of each neuron to each stimulus group, we fitted a novel predictive model: a reduced generalized linear-nonlinear model (GLNM) that takes the frequency modulation and single-tone amplitude as the only two input parameters. The GLNM accurately predicted neuronal responses to previously unheard USVs, and its prediction accuracy was higher than that of an analogous spectrogram-based linear-nonlinear model. The response strength of neurons and the model prediction accuracy were higher for original, rather than temporally transformed, vocalizations. These results indicate that A1 processes original USVs differentially than transformed USVs, indicating preference for temporal statistics of the original vocalizations.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据