Estimating a dynamic state to relate neural spiking activity to behavioral signals during cognitive tasks.
Estimating a dynamic state to relate neural spiking activity to behavioral signals during cognitive tasks.
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DOI：
10.1109/embc.2015.7320203
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Eden UT
中科院分区：
文献类型：
--
作者：
Xinyi Deng;Faghih RT;Barbieri R;Paulk AC;Asaad WF;Brown EN;Dougherty DD;Widge AS;Eskandar EN;Eden UT
神经科学中的一个重要问题是理解高维电生理数据和复杂的、动态的行为数据之间的关系。解决这一问题的一个一般策略是定义描述这种关系的基本认知特征的低维表示。在这里，我们描述了一种通用的状态空间方法来建模和拟合低维认知状态过程，该过程允许我们将各种任务的行为结果与同时记录的跨多个大脑区域的神经活动相关联。特别是，我们将这个模型应用于记录在非人类灵长类动物的外侧前额叶皮质(PFC)和尾状核的数据，因为他们在规则转换任务中执行学习和适应。首先，我们定义了一个与学习相关的认知状态过程的模型，并通过实验估计了这种学习状态的进展。接下来，我们建立了一个点过程广义线性模型，将每个PFC和尾状神经元的放电活动与混合学习状态联系起来。然后，我们使用递归贝叶斯译码算法计算认知状态的后验密度。我们证明了使用简单的种群尖峰的点过程模型可以准确地解码学习状态。我们的分析还允许我们比较PFC和尾状核中不同神经群的解码准确性。
An important question in neuroscience is understanding the relationship between high-dimensional electrophysiological data and complex, dynamic behavioral data. One general strategy to address this problem is to define a low-dimensional representation of essential cognitive features describing this relationship. Here we describe a general state-space method to model and fit a low-dimensional cognitive state process that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas. In particular, we apply this model to data recorded in the lateral prefrontal cortex (PFC) and caudate nucleus of non-human primates as they perform learning and adaptation in a rule-switching task. First, we define a model for a cognitive state process related to learning, and estimate the progression of this learning state through the experiments. Next, we formulate a point process generalized linear model to relate the spiking activity of each PFC and caudate neuron to the stimated learning state. Then, we compute the posterior densities of the cognitive state using a recursive Bayesian decoding algorithm. We demonstrate that accurate decoding of a learning state is possible with a simple point process model of population spiking. Our analyses also allow us to compare decoding accuracy across neural populations in the PFC and caudate nucleus.