Point process models for sequence detection in high-dimensional neural spike trains.
Advances in neural information processing systems
2020; 33: 14350-14361
Sparse sequences of neural spikes are posited to underlie aspects of working memory , motor production , and learning [3, 4]. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience [5-7]. Promising recent work [4, 8] 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.
View details for PubMedID 35002191
View details for PubMedCentralID PMC8734964
A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2020; 68: 2499–2512
View details for DOI 10.1109/TSP.2020.2984163
View details for Web of Science ID 000538021500001