下丘神经元听觉啁啾速度敏感性和振幅调制调谐的计算模型

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2024-09-11 DOI:10.1007/s10827-024-00880-4
Paul W. Mitchell, Laurel H. Carney
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引用次数: 0

摘要

我们展示了下丘(IC)的啁啾-速度敏感性模型,该模型保留了早期模型中建立的对振幅调制(AM)的调谐。速度灵敏度的机制是耳蜗后腹核章鱼细胞的序列检测,生理学研究认为章鱼细胞对不同振幅的跨频输入的到达顺序有优先反应。模型结构基于兴奋性和抑制性输入组合的重合检测。集成电路输出的啁啾敏感性主要受对啁啾敏感的章鱼细胞抑制性输入的强度和时间控制。调幅调谐由调谐到相同频率的抑制和兴奋控制。我们介绍了几个神经元示例,证明了该模型模拟现实啁啾敏感性和调幅调谐的可行性,适用于各种特征频率。此外,我们还探讨了参数变化对模型响应的系统性影响。所提出的模型可用于评估集成电路啁啾速度灵敏度对复杂声音(如语音)反应的贡献。
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A computational model of auditory chirp-velocity sensitivity and amplitude-modulation tuning in inferior colliculus neurons

We demonstrate a model of chirp-velocity sensitivity in the inferior colliculus (IC) that retains the tuning to amplitude modulation (AM) that was established in earlier models. The mechanism of velocity sensitivity is sequence detection by octopus cells of the posteroventral cochlear nucleus, which have been proposed in physiological studies to respond preferentially to the order of arrival of cross-frequency inputs of different amplitudes. Model architecture is based on coincidence detection of a combination of excitatory and inhibitory inputs. Chirp-sensitivity of the IC output is largely controlled by the strength and timing of the chirp-sensitive octopus-cell inhibitory input. AM tuning is controlled by inhibition and excitation that are tuned to the same frequency. We present several example neurons that demonstrate the feasibility of the model in simulating realistic chirp-sensitivity and AM tuning for a wide range of characteristic frequencies. Additionally, we explore the systematic impact of varying parameters on model responses. The proposed model can be used to assess the contribution of IC chirp-velocity sensitivity to responses to complex sounds, such as speech.

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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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