ArticleBiological cybernetics2018
Methods for identification of spike patterns in massively parallel spike trains.
Article in Biological cybernetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A quality measure for repeating multiple-unit spike patterns.Biological cybernetics · 2026Article
- Neurons embedded in loop-like motifs act as central hubs for brain-wide integration.The Journal of physiology · 2026Article
- Out of the single-neuron straitjacket: Neurons within assemblies change selectivity and their reconfiguration underlies dynamic coding.The Journal of physiology · 2025Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Connecting the dots in the zona incerta: A study of neural assemblies and motifs of inter-area coordination in mice.iScience · 2024Article
- Neuromorphic hardware for somatosensory neuroprostheses.Nature communications · 2024Review
- 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
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.Neuron · 2022Article
- Statistical neuroscience in the single trial limit.Current opinion in neurobiology · 2021Review
- Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation.Frontiers in neuroinformatics · 2021Article
- Point process models for sequence detection in high-dimensional neural spike trains.Advances in neural information processing systems · 2020Article
- On the methods for reactivation and replay analysis.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- Article
- Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data.Frontiers in neuroinformatics · 2019Review
- Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data.Frontiers in neuroinformatics · 2018Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.
Indexed as
Identifiers
What 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.