Faghih Rose T, Barbieri Riccardo, Paulk Angelique C, Asaad Wael F, Brown Emery N, Dougherty Darin D, Widge Alik S, Eskandar Emad N, Eden Uri T
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7808-13. doi: 10.1109/EMBC.2015.7320203.
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.
神经科学中的一个重要问题是理解高维电生理数据与复杂动态行为数据之间的关系。解决这个问题的一个通用策略是定义一个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，该过程使我们能够将各种任务的行为结果与多个脑区同时记录的神经活动联系起来。特别是，我们将此模型应用于非人类灵长类动物在执行规则切换任务时学习和适应过程中，在外侧前额叶皮层（PFC）和尾状核中记录的数据。首先，我们定义一个与学习相关的认知状态过程模型，并通过实验估计该学习状态的进展。接下来，我们制定一个点过程广义线性模型，将每个PFC和尾状核神经元的发放活动与估计的学习状态联系起来。然后，我们使用递归贝叶斯解码算法计算认知状态的后验密度。我们证明，使用群体发放的简单点过程模型可以准确解码学习状态。我们的分析还使我们能够比较PFC和尾状核中不同神经群体的解码准确性。
