论文标题
高维神经尖峰火车中序列检测的点过程模型
Point process models for sequence detection in high-dimensional neural spike trains
论文作者
论文摘要
神经尖峰的稀疏序列被认为是工作记忆，运动生产和学习的基础。在统计神经科学中，以无监督的方式发现这些序列是一个长期存在的问题。最近有希望的工作利用了一个备受纪念的非负矩阵分解模型来应对这一挑战。但是，该模型需要离散的峰值时间，使用亚最小二乘标准，并且不为模型预测或估计参数提供不确定性估计。我们通过开发一个点过程模型来解决这些缺陷中的每个缺点，该过程模型表征了单个尖峰水平的细尺度序列，并表示序列出现是连续时间的少数标记事件。序列事件的这种超图像表示为尖峰火车建模开辟了新的可能性。例如，我们将可学习的时间翘曲参数引入模型，以建模不同持续时间的序列，这些序列已在神经回路中进行了实验观察到。我们在Songbird高级声带和啮齿动物海马的实验录音中证明了这些优势。
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience. Promising recent work utilized a convolutive nonnegative matrix factorization model to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model predictions or estimated parameters. We address each of these shortcomings by developing a point process model that characterizes fine-scale sequences at the level of individual spikes and represents sequence occurrences as a small number of marked events in continuous time. This ultra-sparse representation of sequence events opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, which have been experimentally observed in neural circuits. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.
