Neuron's eye view: Inferring features of complex stimuli from neural responses
Neuron's eye view: Inferring features of complex stimuli from neural responses
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DOI：
10.1371/journal.pcbi.1005645
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发表时间：
2017-08-01
影响因子：
4.3
通讯作者：
Pearson, John M.
中科院分区：
文献类型：
--
作者：
Chen, Xin;Beck, Jeffrey M.;Pearson, John M.
在单个神经元水平上研究刺激的神经编码的实验，通常会选择世界上存在的一小部分特征--视觉的对比度和亮度，声音的音调和强度--并组装一个沿这些维度系统变化的刺激集。随后对这些刺激的神经反应的分析通常集中在回归模型上，以实验者控制的特征作为预测因素，以尖峰计数或放电频率作为反应。不幸的是，这种方法需要事先了解特定神经元群体所编码的相关特征。然而，对于像社会互动或自然运动这样复杂的领域，人们对相关的特征空间知之甚少，而任意的先验特征选择可能会导致确认偏差。在这里，我们提出了一个用于探索性数据分析的贝叶斯模型，该模型能够仅基于神经元反应自动识别非结构化刺激中存在的特征。我们的方法在神经活动的潜在状态空间模型中是独一无二的，因为它假设神经元的放电速率对与刺激相关的多个离散时变特征敏感，每个特征都具有马尔可夫(或半马尔可夫)动力学。也就是说，我们将神经活动建模为由多个同时刺激特征驱动，而不是内在神经动力学。我们推导了一种快速的变分贝叶斯推理算法，并证明了它正确地恢复了合成数据中的隐藏特征，以及原型神经数据集中的地面真实刺激特征。为了验证该算法的有效性，我们还将其应用于神经反应的聚类，并成功地恢复了图像集中与猴子和人脸相对应的特征。
Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.