# Point process models for sequence detection in high-dimensional neural spike trains

arXiv: https://arxiv.org/abs/2010.04875 · HTML: https://mlsanity.com/paper/2010.04875/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [EasyTPP: Towards Open Benchmarking Temporal Point Processes](https://mlsanity.com/paper/2307.08097/) (2023)
- [Anticipatory Music Transformer](https://mlsanity.com/paper/2306.08620/) (2023)
- [Deep Continuous-Time State-Space Models for Marked Event Sequences](https://mlsanity.com/paper/2412.19634/) (2024)
- [Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes](https://mlsanity.com/paper/2305.12569/) (2023)
- [Deep Neyman-Scott Processes](https://mlsanity.com/paper/2111.03949/) (2021)
- [Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models](https://mlsanity.com/paper/2201.05044/) (2022)
- [On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes](https://mlsanity.com/paper/2406.00630/) (2024)
- [Variational Inference for Neyman-Scott Processes](https://mlsanity.com/paper/2303.03701/) (2023)
- [Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes](https://mlsanity.com/paper/2308.00858/) (2023)
- [Scalable inference of functional neural connectivity at submillisecond timescales](https://mlsanity.com/paper/2510.20966/) (2025)

## Abstract (arXiv)

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.