ArticlePLoS computational biology2012
State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- A quality measure for repeating multiple-unit spike patterns.Biological cybernetics · 2026Article
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics.Nature communications · 2025Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning.Nature communications · 2025Article
- Third-order entropy for spatiotemporal neural network characterization.Journal of neurophysiology · 2025Article
- Remapping revisited: how the hippocampus represents different spaces.Nature reviews. Neuroscience · 2024Review
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach.PLoS computational biology · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Reconstructing computational system dynamics from neural data with recurrent neural networks.Nature reviews. Neuroscience · 2023Review
- Hippocampal and medial prefrontal ensemble spiking represents episodes and rules in similar task spaces.Cell reports · 2023Article
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach.bioRxiv : the preprint server for biology · 2023Article
- Discovering Low-Dimensional Descriptions of Multineuronal Dependencies.Entropy (Basel, Switzerland) · 2023Article
- Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons.Communications biology · 2023Article
- Geometry of spiking patterns in early visual cortex: a topological data analytic approach.Journal of the Royal Society, Interface · 2022Article
- Hippocampal signals modify orbitofrontal representations to learn new paths.Current biology : CB · 2022Article
- An in silico model for determining the influence of neuronal co-activity on rodent spatial behavior.Journal of neuroscience methods · 2022Article
- Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data.PLoS computational biology · 2022Article
- Mixed vine copula flows for flexible modeling of neural dependencies.Frontiers in neuroscience · 2022Article
- Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships.PLoS computational biology · 2022Article
- A unifying framework for mean-field theories of asymmetric kinetic Ising systems.Nature communications · 2021Article
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4 authors.
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Abstract
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.
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