Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, United States of America.
PLoS Comput Biol. 2011 Oct;7(10):e1002219. doi: 10.1371/journal.pcbi.1002219. Epub 2011 Oct 27.
The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets.
线性感受野描述了从感觉刺激到一维变量的映射，该变量控制神经元的尖峰反应。然而，传统的感受野估计器，如尖峰触发平均，收敛缓慢，通常需要大量的数据。贝叶斯方法通过将估计偏向于先验可能性更高的解决方案来克服这个问题，通常是那些系数较小、平滑或稀疏的解决方案。在这里，我们引入了一种新的贝叶斯感受野估计器，旨在将局部性（关于感受野结构的一种强大的先验信息形式）纳入其中。我们方法的关键是一个层次化的感受野模型，它使用一种称为经验贝叶斯的推断方法，灵活地适应时空和时空频率中的局部结构。我们将我们的方法称为自动定位确定（ALD），并表明它可以准确地恢复各种类型的平滑、稀疏和局部化的感受野。我们将 ALD 应用于视网膜神经节细胞和 V1 简单细胞的神经数据，并发现它的错误率比标准估计器低几个数量级。因此，可以用更少的数据获得具有相当准确性的估计。最后，我们引入了一种在 ALD 先验下进行完全贝叶斯推断的计算高效的马尔可夫链蒙特卡罗（MCMC）算法，为小数据集或噪声数据集提供准确的贝叶斯置信区间。
