ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2011
Spike-train communities: finding groups of similar spike trains.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- A quality measure for repeating multiple-unit spike patterns.Biological cybernetics · 2026Article
- Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework.Frontiers in neuroscience · 2025Article
- An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior.bioRxiv : the preprint server for biology · 2024Article
- In vivo ephaptic coupling allows memory network formation.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Spikebench: An open benchmark for spike train time-series classification.PLoS computational biology · 2023Article
- Cholinergic control of striatal GABAergic microcircuits.Cell reports · 2022Article
- To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data.Journal of neuroscience methods · 2022Article
- Single-cell and ensemble activity of lumbar intermediate and ventral horn interneurons in the spinal air-stepping cat.Journal of neurophysiology · 2022Article
- Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models.PloS one · 2021Article
- Scalable and accurate method for neuronal ensemble detection in spiking neural networks.PloS one · 2021Article
- Non-backtracking walks reveal compartments in sparse chromatin interaction networks.Scientific reports · 2020Article
- Article
- Dynamical networks: Finding, measuring, and tracking neural population activity using network science.Network neuroscience (Cambridge, Mass.) · 2018Article
- Emergent cortical circuit dynamics contain dense, interwoven ensembles of spike sequences.Journal of neurophysiology · 2017Article
- Fast wide-volume functional imaging of engineered in vitro brain tissues.Scientific reports · 2017Article
- Article
- An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics.PLoS computational biology · 2017Article
- Article
- Neuronal Assemblies Evidence Distributed Interactions within a Tactile Discrimination Task in Rats.Frontiers in neural circuits · 2017Article
- Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.Neuron · 2016Article
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1 author.
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Abstract
Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.
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