Detection of Hidden Structures in Nonstationary Spike Trains
Detection of Hidden Structures in Nonstationary Spike Trains
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DOI：
10.1162/neco_a_00109
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发表时间：
2011-05
影响因子：
2.9
通讯作者：
K. Takiyama;M. Okada
中科院分区：
文献类型：
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作者：
K. Takiyama;M. Okada
我们提出了一种算法，同时估计神经状态和非平稳的发射率之间的状态转换使用切换状态空间模型（SSSM）。该算法使我们能够不仅基于平均放电率的不连续变化而且基于放电率的时间分布的不连续变化（例如，时间相关性）。我们构造的估计和学习算法的非高斯SSSM，其非高斯属性所造成的二进制尖峰事件。局部变分方法可以将二元观测过程转化为二次型。变换后的观测过程使我们能够构建一个变分贝叶斯算法，该算法可以基于自动相关性确定来确定神经状态的数量。此外，我们的算法可以估计模型参数，从单次试验数据，使用先验知识的状态转换和发射率。合成数据分析表明，我们的算法有更高的性能估计非平稳发射率比以前的方法。分析还证实，我们的算法可以检测状态转换的时间相关性，这是以前的隐马尔可夫模型无法检测到的过渡的不连续变化的基础上。我们还分析了从内侧颞区记录的神经数据。统计检测到的神经状态可能与已被检测到的瞬态和持续状态一致。估计的参数表明，我们的算法检测的基础上的不连续变化的时间相关性的放电率的状态转换。这些结果表明，我们的算法是有利的，在实际数据分析。
We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into a quadratic form. The transformed observation process enables us to construct a variational Bayes algorithm that can determine the number of neural states based on automatic relevance determination. Additionally, our algorithm can estimate model parameters from single-trial data using a priori knowledge about state transitions and firing rates. Synthetic data analysis reveals that our algorithm has higher performance for estimating nonstationary firing rates than previous methods. The analysis also confirms that our algorithm can detect state transitions on the basis of discontinuous changes in temporal correlation, which are transitions that previous hidden Markov models could not detect. We also analyze neural data recorded from the medial temporal area. The statistically detected neural states probably coincide with transient and sustained states that have been detected heuristically. Estimated parameters suggest that our algorithm detects the state transitions on the basis of discontinuous changes in the temporal correlation of firing rates. These results suggest that our algorithm is advantageous in real-data analysis.