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Cooperation of Deterministic Dynamics and Random Noise in Production of Complex Syntactical Avian Song Sequences: A Neural Network Model

机译:确定性动力学和随机噪声在复杂句法鸟类歌曲序列生产中的合作:神经网络模型

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摘要

How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC), a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf) projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN) that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf–HVC interaction.
机译:大脑如何学习和产生时间序列是神经科学中的一个基本问题。鸟鸣的产生涉及复杂的学习序列,为研究人员提供了一个出色的生物学模型。特别是孟加拉雀科学习的是句法结构非常复杂的歌曲。 HVC(HVC)核是禽类歌曲系统中的运动前核,在产生其歌曲的时间结构方面起着关键作用。根据病灶研究,投射到HVC的界面核(NIf)被认为是造成其歌曲复杂性的重要区域之一。但是,HVC和NIf之间可以产生复杂句法歌曲的交互类型仍然不清楚。为了研究HVC和NIf之间相互作用的功能,我们提出了基于先前生物学证据的神经网络模型。 HVC由循环神经网络(RNN)建模,该网络学习生成歌曲的时间模式。 NIf被建模为向HVC提供听觉反馈并生成馈入HVC的随机噪声的机制。该模型表明,复杂的句法歌曲可以通过RNN确定性动力学与随机噪声之间的简单交互来复制。在当前的研究中,通过比较药理抑制NIf引起的实际鸟类鸣叫的变化与因代表NIf功能的参数的修改而产生的模型鸣叫的变化之间的比较,测试了模型的合理性。该模型的有效性表明,由药理学抑制NIf引起的歌曲变化可以解释为噪声效应与HVC RNN动态反馈效应之间的权衡。这些事实表明,当前模型为NIf-HVC相互作用的功能作用提供了令人信服的假设。

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