The Essential Complexity of Auditory Receptive Fields.
The Essential Complexity of Auditory Receptive Fields.
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DOI：
10.1371/journal.pcbi.1004628
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发表时间：
2015-12
影响因子：
4.3
通讯作者：
David SV
中科院分区：
文献类型：
--
作者：
Thorson IL;Liénard J;David SV
感觉神经元的编码特性通常使用线性有限脉冲响应（FIR）滤波器来建模。对于听觉系统，FIR滤波器在光谱-时间感受野（STRF）中实例化，通常在广义线性模型的框架中。尽管广泛使用FIR STRF，线性滤波器的许多公式可能需要更少的参数，可能允许更有效和准确的模型估计。为了探索这些可选择的STRF结构，我们记录了在自然声音刺激下清醒雪貂听觉皮层的单个神经活动。我们比较了bbb1000个线性STRF架构的性能，评估了它们预测神经对新自然刺激的反应的能力。许多能够优于FIR滤波器。该体系结构的两个基本约束导致了性能的提高：(1)将STRF矩阵分解为少量的光谱和时间滤波器；(2)将分解后的滤波器进行低维参数化。尽管所需参数少于30个，约为FIR滤波器所需参数的10%，但最佳参数化模型在初级和次级听觉皮层中的表现都优于全FIR滤波器。在考虑了有限数据采样的噪声后，这些strf能够解释平均40%的A1响应方差。更简单的模型允许更直接的解释感官调谐特性。他们还显示，与线性模型相比，纳入非线性术语（如短期可塑性）带来了更大的好处。在保持最大预测能力的同时，最小化参数数量的架构提供了对控制听觉皮层功能的基本自由度的深入了解。它们还可以最大限度地提高统计能力，用于描述限制当前听觉模型的附加非线性特性。了解大脑如何解决感官问题可以为语音识别器和图像分类器等自动化系统的开发提供有用的见解。非线性回归和机器学习的最新发展已经产生了强大的算法来表征复杂系统的输入输出关系。然而，感觉神经系统的复杂性，加上实验数据的实际限制，使得对神经数据进行任意复杂的分析变得困难。在这项研究中，我们将分析推向了相反的方向，即更简单的模型。我们想知道一个模型能有多简单，同时还能捕捉到听觉皮层神经元的基本感觉特性。我们发现，广泛使用的光谱-时间接受野的简单公式能够表现得和当前最好的模型一样好。这些更简单的公式定义了新的基集，可以纳入最先进的机器学习算法，以更详尽地探索感官处理。
Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models. Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers. Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems. However, the complexity of sensory neural systems, combined with practical limitations on experimental data, make it difficult to apply arbitrarily complex analyses to neural data. In this study we pushed analysis in the opposite direction, toward simpler models. We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex. We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models. These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing.
影响因子：
3.3
作者：
Depireux, D. A.;Dobbins, H. D.;Marvit, P.;Shechter, B.
通讯作者：
Shechter, B.