Recursive Sparse Point Process Regression With Application to Spectrotemporal Receptive Field Plasticity Analysis
Recursive Sparse Point Process Regression With Application to Spectrotemporal Receptive Field Plasticity Analysis
复制标题
递归稀疏点过程回归及其在谱时间感受野可塑性分析中的应用
DOI：
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发表时间：
2015
影响因子：
5.4
通讯作者：
B. Babadi
中科院分区：
文献类型：
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作者：
Alireza Sheikhattar;J. Fritz;S. Shamma;B. Babadi
考虑了点过程模型的稀疏时变参数向量的在线估计问题，其中观测值和输入分别由二进制和连续时间序列组成.我们构造了一个新的目标函数，通过将遗忘因子机制点过程对数似然执行自适应性和采用l1正则化捕捉稀疏性。我们提供了一个严格的分析的最大化的目标函数，它扩展了压缩感知的保证，我们的设置。我们构造了两个递归滤波器的在线估计的参数向量的基础上近似优化技术，以及一个新的过滤器的统计置信区域的递归计算。仿真研究表明，我们的算法优于几个现有的点过程滤波器的跟踪能力，拟合优度和均方误差。最后，我们将我们的过滤算法实验记录的尖峰数据从雪貂初级听觉皮层在注意力集中的行为在点击率歧视任务。我们的分析提供了新的见解的spectrotemporal感受野可塑性的听觉神经元的时间过程。
We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ l1-regularization to capture the sparsity. We provide a rigorous analysis of the maximizers of the objective function, which extends the guarantees of compressed sensing to our setting. We construct two recursive filters for online estimation of the parameter vectors based on proximal optimization techniques, as well as a novel filter for recursive computation of statistical confidence regions. Simulation studies reveal that our algorithms outperform several existing point process filters in terms of trackability, goodness-of-fit and mean square error. We finally apply our filtering algorithms to experimentally recorded spiking data from the ferret primary auditory cortex during attentive behavior in a click rate discrimination task. Our analysis provides new insights into the time-course of the spectrotemporal receptive field plasticity of the auditory neurons.
影响因子：
2.5
作者：
Truccolo, W;Eden, UT;Brown, EN
通讯作者：
Brown, EN
影响因子：
2.5
作者：
Depireux, DA;Simon, JZ;Shamma, SA
通讯作者：
Shamma, SA