Point process models for sequence detection in high-dimensional neural spike trains
This work addresses a longstanding challenge in statistical neuroscience for researchers analyzing high-dimensional neural data, though it is incremental as it builds on prior convolutive nonnegative matrix factorization models.
The authors tackled the problem of unsupervised detection of sparse neural spike sequences, which are important for memory and learning, by developing a point process model that operates on individual spikes in continuous time, achieving a more accurate representation and enabling features like learnable time warping.
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.