Deep neural networks effectively model neural adaptation to changing background noise and suggest nonlinear noise filtering methods in auditory cortex.
Deep neural networks effectively model neural adaptation to changing background noise and suggest nonlinear noise filtering methods in auditory cortex.
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DOI：
10.1016/j.neuroimage.2022.119819
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发表时间：
2023-02-01
期刊：
影响因子：
5.7
通讯作者：
Mesgarani, Nima
中科院分区：
文献类型：
--
作者：
Mischler, Gavin;Keshishian, Menoua;Bickel, Stephan;Mehta, Ashesh D.;Mesgarani, Nima
人类的听觉系统显示出强大的适应背景噪声突然变化的能力，允许连续的语音理解，尽管背景环境发生了变化。然而，尽管对这种能力进行了全面的研究，但对这一过程的计算还没有被很好地理解，理解复杂系统的第一步是提出一个合适的模型，但是对于听觉系统来说，经典的和容易解释的模型，频谱-时间感受场(STRF)，不能与涉及噪声适应的非线性神经动力学相匹配。在这里，我们利用深度神经网络(DNN)来模拟神经对噪声的适应，展示了它在单个电极和大脑皮层群体水平上再现复杂动力学的有效性。通过仔细检查该模型随着时间的推移进行的类似STRF的计算，我们发现该模型在适应突然的噪声变化时改变了其感受场的增益和形状。我们发现，DNN模型的增益变化使其能够执行自适应增益控制，而谱-时间变化通过进一步改变模型感受场的抑制区域来产生噪声滤波。我们发现，非初级听觉皮质的电极模型在其兴奋区域也表现出噪声滤波变化，这表明沿皮质层级的噪声滤波机制不同。这些发现证明了深度神经网络对复杂的神经适应进行建模的能力，并提供了关于听觉皮质执行的计算的新假设，以便在真实世界的动态环境中实现抗噪语音感知。
The human auditory system displays a robust capacity to adapt to sudden changes in background noise, allowing for continuous speech comprehension despite changes in background environments. However, despite comprehensive studies characterizing this ability, the computations that underly this process are not well understood The first step towards understanding a complex system is to propose a suitable model, but the classical and easily interpreted model for the auditory system, the spectro-temporal receptive field (STRF), cannot match the nonlinear neural dynamics involved in noise adaptation. Here, we utilize a deep neural network (DNN) to mode neural adaptation to noise, illustrating its effectiveness at reproducing the complex dynamics at the levels of both individual electrodes and the cortical population. By closely inspecting the model’s STRF-like computations over time, we find that the model alters both the gain and shape of its receptive field when adapting to a sudden noise change. We show that the DNN model’s gain changes allow it to perform adaptive gain control, while the spectro-temporal change creates noise filtering by altering the inhibitory region of the model’s receptive field Further, we find that models of electrodes in nonprimary auditory cortex also exhibit noise filtering changes in their excitatory regions, suggesting differences in noise filtering mechanisms along the cortical hierarchy. These findings demonstrate the capability of deep neural networks to model complex neural adaptation and offer new hypotheses about the computations the auditory cortex performs to enable noise-robust speech perception in real-world, dynamic environments.
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DOI：
10.1523/jneurosci.5297-12.2013
发表时间：
2013-03-27
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Ding N;Simon JZ
通讯作者：
Simon JZ
音调的皮质编码：最新结果和开放问题。
影响因子：
2.8
作者：
Walker, Kerry M. M.;Bizley, Jennifer K.;King, Andrew J.;Schnupp, Jan W. H.
通讯作者：
Schnupp, Jan W. H.
影响因子：
2.8
作者：
David SV
通讯作者：
David SV
DOI：
10.1523/jneurosci.5249-08.2009
发表时间：
2009-03-18
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
David SV;Mesgarani N;Fritz JB;Shamma SA
通讯作者：
Shamma SA
影响因子：
2.5
作者：
Ingham, NJ;McAlpine, D
通讯作者：
McAlpine, D