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Spatiotemporal Conditional Inference and Hypothesis Tests for Neural Ensemble Spiking Precision

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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An Introduction to Categorical Data Analysis

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Testing Statistical Hypotheses

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Exact inference for categorical data: recent advances and continuing controversies

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A Theorem on Flows in Networks

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Combinatorial Properties of Matrices of Zeros and Ones

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Local Cortical Circuits

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Detecting higher-order interactions among the spiking events in a group of neurons

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Analysis of higher-order neuronal interactions based on conditional inference

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Techniques for extracting single-trial activity patterns from large-scale neural recordings

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Asymptotic enumeration of dense 0–1 matrices with specified line sums

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Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology

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Rapid Neocortical Dynamics: Cellular and Network Mechanisms

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Graphical models for machine learning and digital communication

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Temporal binding and the neural correlates of sensory awareness

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Bootstrap Methods and their Application

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Dynamics of neuronal interactions in monkey cortex in relation to behavioural events

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Weak pairwise correlations imply strongly correlated network states in a neural population

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Spatio-temporal correlations and visual signalling in a complete neuronal population

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Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex

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Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes

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Single-neuron dynamics in human focal epilepsy

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Measuring and interpreting neuronal correlations

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Methods of Information Geometry

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On Closed Testing Procedures with Special Reference to Ordered Analysis of Variance

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Generalized Monte Carlo Significance Tests

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Conservative hypothesis tests and confidence intervals using importance sampling

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The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex

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Neuronal assemblies

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Cortical Representation of Auditory Space: Information-Bearing Features of Spike Patterns

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Conditional modeling and the jitter method of spike resampling

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Trial-to-Trial Variability and Its Effect on Time-Varying Dependency Between Two Neurons

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Statistical Assessment of Time-Varying Dependency Between Two Neurons

inferredcites →https://doi.org/10.1152/jn.00697.2004openalex-snapshot

A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects

inferredcites →https://doi.org/10.1152/jn.1998.80.6.3345openalex-snapshot

Slow Covariations in Neuronal Resting Potentials Can Lead to Artefactually Fast Cross-Correlations in Their Spike Trains

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Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

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Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data

inferredcites →https://doi.org/10.1162/089976603321043720openalex-snapshot

Synchronous Firing and Higher-Order Interactions in Neuron Pool

inferredcites →https://doi.org/10.1162/neco.2008.03-08-730openalex-snapshot

A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains

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A Framework for Evaluating Pairwise and Multiway Synchrony Among Stimulus-Driven Neurons

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Accelerated Spike Resampling for Accurate Multiple Testing Controls

inferredcites →https://doi.org/10.1184/r1/6586928openalex-snapshot

Statistical Identification of Synchronous Spiking

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Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference

inferredcites →https://doi.org/10.1198/jasa.2006.s100openalex-snapshot

Testing Statistical Hypotheses

inferredcites →https://doi.org/10.1201/b14859openalex-snapshot

Spike Timing

inferredcites →https://doi.org/10.1201/b14859-6openalex-snapshot

- Spike Trains as Event Sequences: Fundamental Implications

inferredcites →https://doi.org/10.1214/10-aoas429openalex-snapshot

Assessment of synchrony in multiple neural spike trains using loglinear point process models

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Exact sampling and counting for fixed-margin matrices

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The Roles of Conditioning in Inference

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Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't

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State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

inferredcites →https://doi.org/10.1523/jneurosci.1282-06.2006openalex-snapshot

The Structure of Multi-Neuron Firing Patterns in Primate Retina

inferredcites →https://doi.org/10.1523/jneurosci.1831-12.2012openalex-snapshot

Population Rate Dynamics and Multineuron Firing Patterns in Sensory Cortex

inferredcites →https://doi.org/10.1523/jneurosci.22-16-07297.2002openalex-snapshot

Decreased Neuronal Synchronization during Experimental Seizures

inferredcites →https://doi.org/10.1523/jneurosci.2929-08.2008openalex-snapshot

Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex

inferredcites →https://doi.org/10.1523/jneurosci.2948-05.2006openalex-snapshot

Spike Count Reliability and the Poisson Hypothesis

inferredcites →https://doi.org/10.1523/jneurosci.4567-13.2014openalex-snapshot

Neuronal Ensemble Synchrony during Human Focal Seizures

inferredcites →https://doi.org/10.2140/pjm.1957.7.1073openalex-snapshot

A theorem on flows in networks

inferredcites →https://doi.org/10.2307/2669707openalex-snapshot

Bootstrap Methods and Their Application

inferredcites →https://doi.org/10.3389/fncom.2010.00016openalex-snapshot

Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference

inferredcites →https://doi.org/10.3389/fncom.2010.00127openalex-snapshot

Surrogate spike train generation through dithering in operational time

inferredcites →https://doi.org/10.3389/fncom.2011.00052openalex-snapshot

A statistical description of neural ensemble dynamics

inferredcites →https://doi.org/10.4153/cjm-1957-044-3openalex-snapshot

Combinatorial Properties of Matrices of Zeros and Ones

inferredcites →https://doi.org/10.48550/arxiv.1301.3928openalex-snapshot

Importance sampling for weighted binary random matrices with specified margins

inferredcites →https://doi.org/10.7551/mitpress/3348.001.0001openalex-snapshot

Graphical Models for Machine Learning and Digital Communication

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Monosynaptic inference via finely-timed spikes

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Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

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Towards the neural population doctrine

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From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining

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Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

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Affective memory rehearsal with temporal sequences in amygdala neurons

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Ambiguity and nonidentifiability in the statistical analysis of neural codes

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Computational Neuroscience: Mathematical and Statistical Perspectives

inferred← citeshttps://doi.org/10.1152/jn.00394.2017openalex-snapshot

Emergent cortical circuit dynamics contain dense, interwoven ensembles of spike sequences

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Temporal accuracy of human cortico-cortical interactions

inferred← citeshttps://doi.org/10.1162/neco_a_00399openalex-snapshot

Accelerated Spike Resampling for Accurate Multiple Testing Controls

inferred← citeshttps://doi.org/10.1162/neco_a_00927openalex-snapshot

Spike-Centered Jitter Can Mistake Temporal Structure

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Neuronal Ensemble Synchrony during Human Focal Seizures

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