ArticleAdvances in neural information processing systems2020
Point process models for sequence detection in high-dimensional neural spike trains.
Article in Advances in neural information processing systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 9 citations in OpenAlex.
- Replay of procedural memory is independent of the hippocampus.Nature neuroscience · 2026Article
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- Deep Continuous-Time State-Space Models for Marked Event Sequences.Advances in neural information processing systems · 2025Article
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- Precise Extraction of Neural Motifs Reveals Multiscale, Parallel Encoding Schemes in Auditory Cortex.bioRxiv : the preprint server for biology · 2025Article
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- Interpretable deep learning for deconvolutional analysis of neural signals.bioRxiv : the preprint server for biology · 2024Article
- Dimensionality reduction beyond neural subspaces with slice tensor component analysis.Nature neuroscience · 2024Article
- Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models.Journal of the American Statistical Association · 2024Article
- SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns.PLoS computational biology · 2023Article
- How our understanding of memory replay evolves.Journal of neurophysiology · 2023Review
- Neural ensembles in navigation: From single cells to population codes.Current opinion in neurobiology · 2023Review
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
- The Impact of Multivesicular Release on the Transmission of Sensory Information by Ribbon Synapses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
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- Statistical neuroscience in the single trial limit.Current opinion in neurobiology · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Sparse sequences of neural spikes are posited to underlie aspects of working memory [1], motor production [2], 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 [9] 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 [10]. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.
Identifiers
35002191PMC8734964W3091804413What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.