Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise.
Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise.
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DOI：
10.1371/journal.pcbi.1005281
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发表时间：
2017-02
影响因子：
4.3
通讯作者：
Jaini P
中科院分区：
文献类型：
--
作者：
Burge J;Jaini P
精度最大化分析(AMA)是近年来发展起来的一种贝叶斯理想观测器降维方法。在给定近端刺激(例如视网膜图像)的训练集、响应噪声模型和成本函数的情况下，AMA返回用于从这些刺激中提取最有用的刺激特征以估计用户指定的潜在变量的过滤器(即感受场)。在这里，我们首先贡献了两个显著减少AMA计算时间的技术进步：我们推导了两个流行的估计器适用的成本函数的梯度，并实现了用于过滤器学习的随机梯度下降(AMA-SGD)例程。接下来，我们展示了如何使用该方法来同时探索自然刺激可变性、先验优先于潜变量、噪声功率和成本函数的选择对神经编码的影响。然后，我们研究了AMA独特的属性组合的几何结构，这些属性将其与更著名的统计方法区分开来。使用双目视差估计作为一个具体的测试案例，我们开发的洞察力对于理解与能量模型相关的一大类基本感觉-知觉任务中的神经编码和解码具有普遍意义。具体地说，我们发现，具有缩放加性噪声的非正交(部分冗余)滤波器的性能往往优于具有恒定加性噪声的正交滤波器；非正交滤波器和缩放加性噪声可以交互作用来塑造噪声诱导的刺激编码不确定性，以匹配与任务无关的刺激变异性。因此，我们证明了被认为是生物物理干扰的神经反应的一些属性可以赋予神经系统编码优势。最后，我们推测，如果将其重新用于神经系统识别问题，AMA可能能够克服标准子单元模型估计的根本局限性。随着自然刺激越来越广泛地应用于心理物理和神经生理行为的研究，我们预计像AMA这样的任务特定的特征学习方法将变得越来越重要。在心理物理学和神经生理学中，在实验中操纵的刺激特征通常是基于直觉、反复试验和历史先例来选择的。精确度最大化分析(AMA)是一种贝叶斯理想的观察者方法，用于从神经系统应该选择的自然刺激中确定与任务相关的特征(即过滤器)。换句话说，AMA是一种为特定任务寻找最佳接受域的方法。早期的结果表明，这种方法有可能对神经科学和知觉科学具有基础性的重要性。首先，我们开发了AMA-SGD，这是一种新版本的AMA，它显著减少了滤波器的学习时间，并用它来学习双目视差估计这一经典任务的最优滤波器。然后，我们发现，自然刺激的可测量的、与任务相关的属性是最优过滤器的最重要的决定因素；改变先验、成本函数和内部噪声对过滤器的影响很小。最后，我们证明了神经系统的一些普遍存在的特性，通常被认为是生物物理滋扰，实际上可以提高神经编码的保真度。特别是，我们首次证明了缩放的加性噪声和冗余(非正交)滤波器可以相互作用来塑造由于内部噪声而产生的不确定性，以匹配与任务无关的自然刺激可变性。
Accuracy Maximization Analysis (AMA) is a recently developed Bayesian ideal observer method for task-specific dimensionality reduction. Given a training set of proximal stimuli (e.g. retinal images), a response noise model, and a cost function, AMA returns the filters (i.e. receptive fields) that extract the most useful stimulus features for estimating a user-specified latent variable from those stimuli. Here, we first contribute two technical advances that significantly reduce AMA’s compute time: we derive gradients of cost functions for which two popular estimators are appropriate, and we implement a stochastic gradient descent (AMA-SGD) routine for filter learning. Next, we show how the method can be used to simultaneously probe the impact on neural encoding of natural stimulus variability, the prior over the latent variable, noise power, and the choice of cost function. Then, we examine the geometry of AMA’s unique combination of properties that distinguish it from better-known statistical methods. Using binocular disparity estimation as a concrete test case, we develop insights that have general implications for understanding neural encoding and decoding in a broad class of fundamental sensory-perceptual tasks connected to the energy model. Specifically, we find that non-orthogonal (partially redundant) filters with scaled additive noise tend to outperform orthogonal filters with constant additive noise; non-orthogonal filters and scaled additive noise can interact to sculpt noise-induced stimulus encoding uncertainty to match task-irrelevant stimulus variability. Thus, we show that some properties of neural response thought to be biophysical nuisances can confer coding advantages to neural systems. Finally, we speculate that, if repurposed for the problem of neural systems identification, AMA may be able to overcome a fundamental limitation of standard subunit model estimation. As natural stimuli become more widely used in the study of psychophysical and neurophysiological performance, we expect that task-specific methods for feature learning like AMA will become increasingly important. In psychophysics and neurophysiology, the stimulus features that are manipulated in experiments are often selected based on intuition, trial-and-error, and historical precedence. Accuracy Maximization Analysis (AMA) is a Bayesian ideal observer method for determining the task-relevant features (i.e. filters) from natural stimuli that nervous systems should select for. In other words, AMA is a method for finding optimal receptive fields for specific tasks. Early results suggest that this method has the potential to be of fundamental importance to neuroscience and perception science. First, we develop AMA-SGD, a new version of AMA that significantly reduces filter-learning time, and use it to learn optimal filters for the classic task of binocular disparity estimation. Then, we find that measureable, task-relevant properties of natural stimuli are the most important determinants of the optimal filters; changes to the prior, cost function, and internal noise have little effect on the filters. Last, we demonstrate that some ubiquitous properties of neural systems, generally thought to be biophysical nuisances, can actually improve the fidelity of neural codes. In particular, we show for the first time that scaled additive noise and redundant (non-orthogonal) filters can interact to sculpt uncertainty due to internal noise to match task-irrelevant natural stimulus variability.