Burge Johannes, Jaini Priyank
Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America.
Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, United States of America.
PLoS Comput Biol. 2017 Feb 8;13(2):e1005281. doi: 10.1371/journal.pcbi.1005281. eCollection 2017 Feb.
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.
精度最大化分析（AMA）是一种最近开发的用于特定任务降维的贝叶斯理想观察者方法。给定一组近端刺激（例如视网膜图像）的训练集、一个响应噪声模型和一个成本函数，AMA会返回滤波器（即感受野），这些滤波器能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。在此，我们首先贡献了两项技术进展，显著减少了AMA的计算时间：我们推导了两种流行估计器适用的成本函数梯度，并实现了用于滤波器学习的随机梯度下降（AMA - SGD）例程。接下来，我们展示了该方法如何用于同时探究自然刺激变异性、潜在变量的先验、噪声功率以及成本函数选择对神经编码的影响。然后，我们研究了AMA独特属性组合的几何结构，这些属性将其与更知名的统计方法区分开来。以双目视差估计作为一个具体的测试案例，我们得出了一些见解，这些见解对于理解与能量模型相关的广泛基础感官 - 感知任务中的神经编码和解码具有普遍意义。具体而言，我们发现具有缩放加性噪声的非正交（部分冗余）滤波器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相互作用，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，我们表明一些被认为是生物物理干扰的神经反应属性可以赋予神经系统编码优势。最后，我们推测，如果将AMA重新用于神经系统识别问题，它可能能够克服标准亚单位模型估计的一个基本限制。随着自然刺激在心理物理学和神经生理学性能研究中越来越广泛地使用，我们预计像AMA这样的特定任务特征学习方法将变得越来越重要。
