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Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

https://doi.org/10.1101/671909

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What this work says

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

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Lapicque’s 1907 paper: from frogs to integrate-and-fire

inferredcites →https://doi.org/10.1007/s00422-008-0244-yopenalex-snapshot

Spike-train spectra and network response functions for non-linear integrate-and-fire neurons

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Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data

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Modeling the impact of common noise inputs on the network activity of retinal ganglion cells

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Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity

inferredcites →https://doi.org/10.1016/j.conb.2011.10.001openalex-snapshot

Towards reliable spike-train recordings from thousands of neurons with multielectrodes

inferredcites →https://doi.org/10.1016/j.jneumeth.2007.10.012openalex-snapshot

Assessing transient cross-frequency coupling in EEG data

inferredcites →https://doi.org/10.1016/j.jneumeth.2007.11.006openalex-snapshot

A benchmark test for a quantitative assessment of simple neuron models

inferredcites →https://doi.org/10.1016/j.jneumeth.2010.03.024openalex-snapshot

Population decoding of motor cortical activity using a generalized linear model with hidden states

inferredcites →https://doi.org/10.1016/j.neuron.2009.03.014openalex-snapshot

Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations

inferredcites →https://doi.org/10.1016/j.neuron.2016.05.039openalex-snapshot

Analysis of Neuronal Spike Trains, Deconstructed

inferredcites →https://doi.org/10.1016/j.neuron.2017.05.025openalex-snapshot

Neural Manifolds for the Control of Movement

inferredcites →https://doi.org/10.1023/a:1008925309027openalex-snapshot

Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons

inferredcites →https://doi.org/10.1038/nature07140openalex-snapshot

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

inferredcites →https://doi.org/10.1038/nature11129openalex-snapshot

Neural population dynamics during reaching

inferredcites →https://doi.org/10.1038/nature12742openalex-snapshot

Context-dependent computation by recurrent dynamics in prefrontal cortex

inferredcites →https://doi.org/10.1038/nature14273openalex-snapshot

Diverse coupling of neurons to populations in sensory cortex

inferredcites →https://doi.org/10.1038/nn.2735openalex-snapshot

Multiple models to capture the variability in biological neurons and networks

inferredcites →https://doi.org/10.1038/nn.3431openalex-snapshot

Temporal whitening by power-law adaptation in neocortical neurons

inferredcites →https://doi.org/10.1038/nn.3776openalex-snapshot

Dimensionality reduction for large-scale neural recordings

inferredcites →https://doi.org/10.1038/nn.4268openalex-snapshot

Spike sorting for large, dense electrode arrays

inferredcites →https://doi.org/10.1038/s41467-017-02717-4openalex-snapshot

Generalized leaky integrate-and-fire models classify multiple neuron types

inferredcites →https://doi.org/10.1038/s41592-018-0109-9openalex-snapshot

Inferring single-trial neural population dynamics using sequential auto-encoders

inferredcites →https://doi.org/10.1088/1742-5468/2013/03/p03005openalex-snapshot

The effect of nonstationarity on models inferred from neural data

inferredcites →https://doi.org/10.1093/comjnl/7.4.308openalex-snapshot

A Simplex Method for Function Minimization

inferredcites →https://doi.org/10.1101/261016openalex-snapshot

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

inferredcites →https://doi.org/10.1103/physreve.66.051917openalex-snapshot

Population dynamics of interacting spiking neurons

inferredcites →https://doi.org/10.1103/physreve.86.051903openalex-snapshot

Estimating nonstationary input signals from a single neuronal spike train

inferredcites →https://doi.org/10.1103/physreve.89.022705openalex-snapshot

Analog and digital codes in the brain

inferredcites →https://doi.org/10.1109/5.18626openalex-snapshot

A tutorial on hidden Markov models and selected applications in speech recognition

inferredcites →https://doi.org/10.1109/tac.1974.1100705openalex-snapshot

A new look at the statistical model identification

inferredcites →https://doi.org/10.1113/jphysiol.1952.sp004764openalex-snapshot

A quantitative description of membrane current and its application to conduction and excitation in nerve

inferredcites →https://doi.org/10.1126/science.1181936openalex-snapshot

How Good Are Neuron Models?

inferredcites →https://doi.org/10.1126/science.aaa4056openalex-snapshot

Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

inferredcites →https://doi.org/10.1152/jn.00641.2003openalex-snapshot

Alternative to Hand-Tuning Conductance-Based Models: Construction and Analysis of Databases of Model 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.01107.2007openalex-snapshot

DynamicI-VCurves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces

inferredcites →https://doi.org/10.1152/jn.90941.2008openalex-snapshot

Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity

inferredcites →https://doi.org/10.1162/neco_a_00118openalex-snapshot

Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems

inferredcites →https://doi.org/10.1371/journal.pcbi.1002385openalex-snapshot

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

inferredcites →https://doi.org/10.1371/journal.pcbi.1002775openalex-snapshot

Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons

inferredcites →https://doi.org/10.1371/journal.pcbi.1004165openalex-snapshot

Experimentally Verified Parameter Sets for Modelling Heterogeneous Neocortical Pyramidal-Cell Populations

inferredcites →https://doi.org/10.1371/journal.pcbi.1004275openalex-snapshot

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

inferredcites →https://doi.org/10.1371/journal.pcbi.1005309openalex-snapshot

Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations

inferredcites →https://doi.org/10.1371/journal.pcbi.1005390openalex-snapshot

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs

inferredcites →https://doi.org/10.1371/journal.pcbi.1005545openalex-snapshot

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation

inferredcites →https://doi.org/10.1523/jneurosci.18-10-03870.1998openalex-snapshot

The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

inferredcites →https://doi.org/10.1523/jneurosci.23-37-11628.2003openalex-snapshot

How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs

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/10.48550/arxiv.1902.04420openalex-snapshot

Learning interpretable continuous-time models of latent stochastic dynamical systems

inferredcites →https://doi.org/10.6080/k0nc5z4xopenalex-snapshot

Utah array extracellular recordings of spontaneous and visually evoked activity from anesthetized macaque primary visual cortex (V1).

What points at this work

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

inferred← citeshttps://doi.org/10.1038/s41467-019-12572-0openalex-snapshot

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

inferred← citeshttps://doi.org/10.1101/261016openalex-snapshot

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

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