Estimating a Separably Markov Random Field from Binary Observations.
Estimating a Separably Markov Random Field from Binary Observations.
复制标题
从二元观测估计可分离马尔可夫随机场。
DOI：
10.1162/neco_a_01059
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发表时间：
2018
影响因子：
2.9
通讯作者：
Ba,Demba
中科院分区：
文献类型：
--
作者：
Zhang,Yingzhuo;Malem-Shinitski,Noa;Allsop,StephenA;MTye,Kay;Ba,Demba
神经科学的一个基本问题是描述神经回路中神经元的脉冲动态，该回路涉及对刺激或偶然性的学习。当前分析神经尖峰数据的方法的一个关键限制是需要随着时间或试验而崩溃神经活动，这可能导致与理解神经元或电路功能相关的信息丢失。我们介绍了一种新的方法，不仅可以确定伴随神经元偶然性学习的试验对试验动态，还可以确定这种学习相对于条件刺激的开始的延迟。该方法的核心是神经脉冲光栅的可分离二维随机场（RF）模型，其中神经元随时间和试验的联合条件强度函数依赖于两个独立但并行发展的潜在马尔可夫状态序列。估计状态空间模型的经典工具不能很容易地应用于我们的二维可分离射频模型。我们开发了有效的统计和计算工具来估计可分离的二维射频模型的参数。我们将这些数据应用于从前额叶皮层的神经元收集的实验中，该实验旨在描述小鼠恐惧联想学习的神经基础。总的来说，可分离的二维射频模型提供了伴随偶发事件学习的神经尖峰动力学的详细、可解释的特征。
A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to the onset of a conditioned stimulus. The backbone of the method is a separable two-dimensional (2D) random field (RF) model of neural spike rasters, in which the joint conditional intensity function of a neuron over time and trials depends on two latent Markovian state sequences that evolve separately but in parallel. Classical tools to estimate state-space models cannot be applied readily to our 2D separable RF model. We develop efficient statistical and computational tools to estimate the parameters of the separable 2D RF model. We apply these to data collected from neurons in the prefrontal cortex in an experiment designed to characterize the neural underpinnings of the associative learning of fear in mice. Overall, the separable 2D RF model provides a detailed, interpretable characterization of the dynamics of neural spiking that accompany the learning of a contingency.