ArticlePLoS computational biology2018
Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Replay of procedural memory is independent of the hippocampus.Nature neuroscience · 2026Article
- A likelihood-based method for identifying replay from spike sequences.Nature communications · 2026Article
- Article
- Multimodal evaluation of network activity and optogenetic interventions in human hippocampal slices.Nature neuroscience · 2024Article
- Beyond Correlation: Optimal Transport Metrics For Characterizing Representational Stability and Remapping in Neurons Encoding Spatial Memory.bioRxiv : the preprint server for biology · 2023Article
- 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
- Beyond correlation: optimal transport metrics for characterizing representational stability and remapping in neurons encoding spatial memory.Frontiers in cellular neuroscience · 2023Article
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
- Improved visualization of high-dimensional data using the distance-of-distance transformation.PLoS computational biology · 2022Article
- Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks.Entropy (Basel, Switzerland) · 2021Article
- A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity.Biology · 2021Article
- Formalizing planning and information search in naturalistic decision-making.Nature neuroscience · 2021Review
- Point process models for sequence detection in high-dimensional neural spike trains.Advances in neural information processing systems · 2020Article
- Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding.eLife · 2020Article
- On the methods for reactivation and replay analysis.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- Bayesian inference of neuronal assemblies.PLoS computational biology · 2019Article
- Article
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3 authors.
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Abstract
Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern "noise" spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.
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