图 书 馆
基于参数先验知识的二值尖峰序列数据贝叶斯熵估计Bayesian entropy estimation for binary spike train data using parametric prior knowledge |
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| 课程网址: | http://videolectures.net/machine_archer_bayesian_entropy/ |
| 主讲教师: | Evan W.Archer |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2014-11-07 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Shannon's entropy is a basic quantity in information theory, and a fundamental building block for the analysis of neural codes. Estimating the entropy of a discrete distribution from samples is an important and difficult problem that has received considerable attention in statistics and theoretical neuroscience. However, neural responses have characteristic statistical structure that generic entropy estimators fail to exploit. For example, existing Bayesian entropy estimators make the naive assumption that all spike words are equally likely a priori, which makes for an inefficient allocation of prior probability mass in cases where spikes are sparse. Here we develop Bayesian estimators for the entropy of binary spike trains using priors designed to flexibly exploit the statistical structure of simultaneously-recorded spike responses. We define two prior distributions over spike words using mixtures of Dirichlet distributions centered on simple parametric models. The parametric model captures high-level statistical features of the data, such as the average spike count in a spike word, which allows the posterior over entropy to concentrate more rapidly than with standard estimators (e.g., in cases where the probability of spiking differs strongly from 0.5). Conversely, the Dirichlet distributions assign prior mass to distributions far from the parametric model, ensuring consistent estimates for arbitrary distributions. We devise a compact representation of the data and prior that allow for computationally efficient implementations of Bayesian least squares and empirical Bayes entropy estimators with large numbers of neurons. We apply these estimators to simulated and real neural data and show that they substantially outperform traditional methods. |
| 关 键 词: | 神经代码; 尖峰稀疏; 计算效率 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-20：chenjy |
| 最后编审: | 2022-12-20：chenjy |
| 阅读次数: | 12 |