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Inferring and validating mechanistic models of neural microcircuits based on spike-train data
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1 of the 116 references listed on this page is in the Retraction Watch database as retracted (export 2026-10-28): 10.7554/elife.12248 (notice 2018-04-12).
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Maximum Likelihood Estimation and Inference
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Green fluorescent protein expression and colocalization with calretinin, parvalbumin, and somatostatin in the GAD67‐GFP knock‐in mouse
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The Fokker-Planck Equation: Methods of Solution and Applications
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Computing Qualitatively Correct Approximations of Balance Laws: Exponential-Fit, Well-Balanced and Asymptotic-Preserving
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A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input
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Lapicque’s 1907 paper: from frogs to integrate-and-fire
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Spike-train spectra and network response functions for non-linear integrate-and-fire neurons
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The quantitative single-neuron modeling competition
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Firing patterns in the adaptive exponential integrate-and-fire model
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Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro
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Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data
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Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings
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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
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From the statistics of connectivity to the statistics of spike times in neuronal networks
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Synaptic Mechanisms of Forward Suppression in Rat Auditory Cortex
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High-Frequency Organization and Synchrony of Activity in the Purkinje Cell Layer of the Cerebellum
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Analysis of Neuronal Spike Trains, Deconstructed
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Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks
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Circuit Models of Low-Dimensional Shared Variability in Cortical Networks
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Introduction to Theoretical Neurobiology
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Finite Volume Methods for Hyperbolic Problems
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Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
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Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons
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Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex
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Spatio-temporal correlations and visual signalling in a complete neuronal population
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Equalizing excitation–inhibition ratios across visual cortical neurons
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Multiple models to capture the variability in biological neurons and networks
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Measuring and interpreting neuronal correlations
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Slow dynamics and high variability in balanced cortical networks with clustered connections
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The mechanics of state-dependent neural correlations
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The spatial structure of correlated neuronal variability
inferredcites →https://doi.org/10.1038/nrn1516openalex-snapshot
Ca2+-activated K+ channels: molecular determinants and function of the SK family
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/s41467-017-02718-3openalex-snapshot
Systematic generation of biophysically detailed models for diverse cortical neuron types
inferredcites →https://doi.org/10.1038/s41467-018-04839-9openalex-snapshot
Go/No-Go task engagement enhances population representation of target stimuli in primary auditory cortex
inferredcites →https://doi.org/10.1073/pnas.0712231105openalex-snapshot
Large-scale model of mammalian thalamocortical systems
inferredcites →https://doi.org/10.1073/pnas.0906705106openalex-snapshot
Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods
inferredcites →https://doi.org/10.1073/pnas.1015165108openalex-snapshot
Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition
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Synthesizing cognition in neuromorphic electronic systems
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A neuromorphic network for generic multivariate data classification
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Inferring cortical function in the mouse visual system through large-scale systems neuroscience
inferredcites →https://doi.org/10.1080/net.12.2.199.213openalex-snapshot
A simple white noise analysis of neuronal light responses
inferredcites →https://doi.org/10.1088/1742-5468/2013/03/p03008openalex-snapshot
Reconstruction of sparse connectivity in neural networks from spike train covariances
inferredcites →https://doi.org/10.1093/cercor/bhs358openalex-snapshot
The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model
inferredcites →https://doi.org/10.1093/comjnl/7.4.308openalex-snapshot
A Simplex Method for Function Minimization
inferredcites →https://doi.org/10.1101/217976openalex-snapshot
Circuit models of low dimensional shared variability in cortical networks
inferredcites →https://doi.org/10.1101/671909openalex-snapshot
Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models
inferredcites →https://doi.org/10.1103/physreve.66.051917openalex-snapshot
Population dynamics of interacting spiking neurons
inferredcites →https://doi.org/10.1103/physreve.69.051918openalex-snapshot
Effects of synaptic conductance on the voltage distribution and firing rate of 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/physrevlett.121.054101openalex-snapshot
Inferring Network Connectivity from Event Timing Patterns
inferredcites →https://doi.org/10.1103/physrevlett.98.148101openalex-snapshot
Diverse Population-Bursting Modes of Adapting Spiking Neurons
inferredcites →https://doi.org/10.1109/mm.2018.112130359openalex-snapshot
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
inferredcites →https://doi.org/10.1109/tac.1974.1100705openalex-snapshot
A new look at the statistical model identification
inferredcites →https://doi.org/10.1109/ted.2016.2598413openalex-snapshot
A Mini Review of Neuromorphic Architectures and Implementations
inferredcites →https://doi.org/10.1113/jp271518openalex-snapshot
Time‐invariant feed‐forward inhibition of Purkinje cells in the cerebellar cortexin vivo
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/10.1113/jphysiol.1984.sp015378openalex-snapshot
Control of the repetitive discharge of rat CA 1 pyramidal neurones in vitro.
inferredcites →https://doi.org/10.1126/science.1179867openalex-snapshot
Decorrelated Neuronal Firing in Cortical Microcircuits
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/10.1126/science.aab4113openalex-snapshot
Spiking neurons can discover predictive features by aggregate-label learning
inferredcites →https://doi.org/10.1152/jn.00453.2006openalex-snapshot
Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical Neurons
inferredcites →https://doi.org/10.1152/jn.00586.2013openalex-snapshot
How adaptation currents change threshold, gain, and variability of neuronal spiking
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.00686.2005openalex-snapshot
Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
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.00830.2010openalex-snapshot
Interspike interval distributions of spiking neurons driven by fluctuating inputs
inferredcites →https://doi.org/10.1152/jn.00955.2002openalex-snapshot
From Subthreshold to Firing-Rate Resonance
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.1988.59.2.450openalex-snapshot
Slow conductances in neurons from cat sensorimotor cortex in vitro and their role in slow excitability changes
inferredcites →https://doi.org/10.1152/jn.1992.67.1.216openalex-snapshot
Calcium-dependent potassium currents in neurons from cat sensorimotor cortex
inferredcites →https://doi.org/10.1152/jn.2000.84.1.401openalex-snapshot
Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements
inferredcites →https://doi.org/10.1162/0899766042321797openalex-snapshot
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
inferredcites →https://doi.org/10.1162/089976699300016179openalex-snapshot
Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates
inferredcites →https://doi.org/10.1162/neco_a_00196openalex-snapshot
Estimating Parameters of Generalized Integrate-and-Fire Neurons from the Maximum Likelihood of Spike Trains
inferredcites →https://doi.org/10.1371/journal.pcbi.1000850openalex-snapshot
A Threshold Equation for Action Potential Initiation
inferredcites →https://doi.org/10.1371/journal.pcbi.1001056openalex-snapshot
From Spiking Neuron Models to Linear-Nonlinear Models
inferredcites →https://doi.org/10.1371/journal.pcbi.1002059openalex-snapshot
How Structure Determines Correlations in Neuronal Networks
inferredcites →https://doi.org/10.1371/journal.pcbi.1002408openalex-snapshot
Impact of Network Structure and Cellular Response on Spike Time Correlations
inferredcites →https://doi.org/10.1371/journal.pcbi.1002596openalex-snapshot
Decorrelation of Neural-Network Activity by Inhibitory Feedback
inferredcites →https://doi.org/10.1371/journal.pcbi.1002667openalex-snapshot
The Spatial Structure of Stimuli Shapes the Timescale of Correlations in Population Spiking Activity
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.1003301openalex-snapshot
A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks
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.1004167openalex-snapshot
Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments
inferredcites →https://doi.org/10.1371/journal.pcbi.1004197openalex-snapshot
The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information
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.1004761openalex-snapshot
Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons
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/10.1371/journal.pcbi.1005583openalex-snapshot
Linking structure and activity in nonlinear spiking networks
inferredcites →https://doi.org/10.1371/journal.pcbi.1005738openalex-snapshot
Estimating short-term synaptic plasticity from pre- and postsynaptic spiking
inferredcites →https://doi.org/10.1371/journal.pcbi.1006381openalex-snapshot
Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings
inferredcites →https://doi.org/10.1371/journal.pcbi.1006490openalex-snapshot
Predicting how and when hidden neurons skew measured synaptic interactions
inferredcites →https://doi.org/10.1371/journal.pcbi.1006974openalex-snapshot
Weak electric fields promote resonance in neuronal spiking activity: Analytical results from two-compartment cell and network models
inferredcites →https://doi.org/10.1523/jneurosci.1275-09.2009openalex-snapshot
How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains
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.3305-05.2005openalex-snapshot
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model
inferredcites →https://doi.org/10.1523/jneurosci.3924-14.2015openalex-snapshot
Neuronal Morphology Generates High-Frequency Firing Resonance
inferredcites →https://doi.org/10.3389/fninf.2014.00006openalex-snapshot
Equation-oriented specification of neural models for simulations
inferredcites →https://doi.org/10.3389/fnins.2011.00061openalex-snapshot
Motor Planning under Unpredictable Reward: Modulations of Movement Vigor and Primate Striatum Activity
inferredcites →https://doi.org/10.3389/fnins.2013.00011openalex-snapshot
Six Networks on a Universal Neuromorphic Computing Substrate
inferredcites →https://doi.org/10.3389/fnins.2014.00010openalex-snapshot
An efficient automated parameter tuning framework for spiking neural networks
inferredcites →https://doi.org/10.3389/fnins.2014.00405openalex-snapshot
A systematic framework for functional connectivity measures
inferredcites →https://doi.org/10.3389/neuro.01.1.1.001.2007openalex-snapshot
A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data
inferredcites →https://doi.org/10.48550/arxiv.1711.01861openalex-snapshot
Flexible statistical inference for mechanistic models of neural dynamics
inferredcites →https://doi.org/10.7554/elife.12248openalex-snapshot
RETRACTED: A mathematical model explains saturating axon guidance responses to molecular gradients
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Inhibitory control of correlated intrinsic variability in cortical networks
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Genetic evidence that Nkx2.2 acts primarily downstream of Neurog3 in pancreatic endocrine lineage development
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Attentional modulation of neuronal variability in circuit models of cortex
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Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10827-022-00831-xopenalex-snapshot
Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks
inferred← citeshttps://doi.org/10.1101/2020.12.09.413401openalex-snapshot
Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis
inferred← citeshttps://doi.org/10.1101/671909openalex-snapshot
Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models
inferred← citeshttps://doi.org/10.1523/jneurosci.1482-19.2020openalex-snapshot
Modeling the Short-Term Dynamics ofin VivoExcitatory Spike Transmission
inferred← citeshttps://doi.org/10.1523/jneurosci.2291-18.2019openalex-snapshot
Resting-State Neural Firing Rate Is Linked to Cardiac-Cycle Duration in the Human Cingulate and Parahippocampal Cortices
inferred← citeshttps://doi.org/10.51628/001c.90831openalex-snapshot
Circumstantial evidence and explanatory models for synapses in large-scale spike recordings
inferred← citeshttps://doi.org/10.7554/elife.56261openalex-snapshot
Training deep neural density estimators to identify mechanistic models of neural dynamics
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