Training deep neural density estimators to identify mechanistic models of neural dynamics
暂无分享,去创建一个
William F. Podlaski | David S. Greenberg | J. Macke | T. Vogels | P. J. Gonçalves | Jan-Matthis Lueckmann | Michael Deistler | M. Nonnenmacher | Kaan Öcal | G. Bassetto | C. Chintaluri | S. Haddad
[1] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.
[2] J. B. Rosen. The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints , 1960 .
[3] D. Rubin. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .
[4] J. P. Jones,et al. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.
[5] W. R. Foster,et al. Significance of conductances in Hodgkin-Huxley models. , 1993, Journal of neurophysiology.
[6] H. Sompolinsky,et al. Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.
[7] Giuseppe De Nicolao,et al. Nonparametric input estimation in physiological systems: Problems, methods, and case studies , 1997, Autom..
[8] E N Brown,et al. A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.
[9] M. Feldman,et al. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. , 1999, Molecular biology and evolution.
[10] E J Chichilnisky,et al. A simple white noise analysis of neuronal light responses , 2001, Network.
[11] E. Marder,et al. Global Structure, Robustness, and Modulation of Neuronal Models , 2001, The Journal of Neuroscience.
[12] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[13] E. Marder,et al. Failure of averaging in the construction of a conductance-based neuron model. , 2002, Journal of neurophysiology.
[14] Paul Marjoram,et al. Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[15] Eve Marder,et al. Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. , 2003, Journal of neurophysiology.
[16] Marco Laumanns,et al. PISA: A Platform and Programming Language Independent Interface for Search Algorithms , 2003, EMO.
[17] Bruce R. Johnson,et al. Activity-Independent Homeostasis in Rhythmically Active Neurons , 2003, Neuron.
[18] L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models , 2004, Network.
[19] E. Marder,et al. Similar network activity from disparate circuit parameters , 2004, Nature Neuroscience.
[20] Hiroaki Kitano,et al. Biological robustness , 2008, Nature Reviews Genetics.
[21] Eckart Zitzler,et al. Indicator-Based Selection in Multiobjective Search , 2004, PPSN.
[22] Alain Destexhe,et al. Nonlinear Thermodynamic Models of Voltage-Dependent Currents , 2000, Journal of Computational Neuroscience.
[23] E J Chichilnisky,et al. Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model , 2005, The Journal of Neuroscience.
[24] Uri T Eden,et al. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.
[25] John Guckenheimer,et al. Activity-independent coregulation of IA and Ih in rhythmically active neurons. , 2005, Journal of neurophysiology.
[26] L. Abbott,et al. Neural network dynamics. , 2005, Annual review of neuroscience.
[27] Liam Paninski,et al. Efficient estimation of detailed single-neuron models. , 2006, Journal of neurophysiology.
[28] Erik De Schutter,et al. Complex Parameter Landscape for a Complex Neuron Model , 2006, PLoS Comput. Biol..
[29] Donald B. Rubin,et al. Validation of Software for Bayesian Models Using Posterior Quantiles , 2006 .
[30] Michael J. Berry,et al. Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.
[31] E. Marder,et al. Variability, compensation and homeostasis in neuron and network function , 2006, Nature Reviews Neuroscience.
[32] Tim Gollisch,et al. Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction , 2006, Science.
[33] Eve Marder,et al. Structure and visualization of high-dimensional conductance spaces. , 2006, Journal of neurophysiology.
[34] Jonathan W. Pillow,et al. Likelihood-based approaches to modeling the neural code , 2007 .
[35] Henry Markram,et al. A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data , 2007, Front. Neurosci..
[36] Christopher R. Myers,et al. Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..
[37] J. Gold,et al. The neural basis of decision making. , 2007, Annual review of neuroscience.
[38] Mark M. Tanaka,et al. Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.
[39] R. Peri,et al. High-throughput electrophysiology: an emerging paradigm for ion-channel screening and physiology , 2008, Nature Reviews Drug Discovery.
[40] Roger Ratcliff,et al. The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.
[41] W. M. Keck,et al. Highly Selective Receptive Fields in Mouse Visual Cortex , 2008, The Journal of Neuroscience.
[42] Eero P. Simoncelli,et al. Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.
[43] Henry Markram,et al. Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons , 2008, Biological Cybernetics.
[44] John P. Cunningham,et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.
[45] Xiao-Jing Wang. Decision Making in Recurrent Neuronal Circuits , 2008, Neuron.
[46] Liam Paninski,et al. Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings , 2009, PLoS Comput. Biol..
[47] C. Robert,et al. Adaptive approximate Bayesian computation , 2008, 0805.2256.
[48] L. F. Abbott,et al. Generating Coherent Patterns of Activity from Chaotic Neural Networks , 2009, Neuron.
[49] E. Marder,et al. How Multiple Conductances Determine Electrophysiological Properties in a Multicompartment Model , 2009, The Journal of Neuroscience.
[50] S. Wood. Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.
[51] Matthias Bethge,et al. Bayesian Inference for Generalized Linear Models for Spiking Neurons , 2010, Front. Comput. Neurosci..
[52] Olivier François,et al. Non-linear regression models for Approximate Bayesian Computation , 2008, Stat. Comput..
[53] E. Marder,et al. Compensation for Variable Intrinsic Neuronal Excitability by Circuit-Synaptic Interactions , 2010, The Journal of Neuroscience.
[54] Wulfram Gerstner,et al. The influence of structure on the response properties of biologically plausible neural network models , 2011, BMC Neuroscience.
[55] John P. Cunningham,et al. Empirical models of spiking in neural populations , 2011, NIPS.
[56] Bertrand Fontaine,et al. Fitting Neuron Models to Spike Trains , 2011, Front. Neurosci..
[57] E. Marder,et al. Multiple models to capture the variability in biological neurons and networks , 2011, Nature Neuroscience.
[58] E. Marder. Variability, compensation, and modulation in neurons and circuits , 2011, Proceedings of the National Academy of Sciences.
[59] Henry Markram,et al. Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties , 2011, PLoS Comput. Biol..
[60] Nicolas Chopin,et al. Expectation Propagation for Likelihood-Free Inference , 2011, 1107.5959.
[61] A. Litwin-Kumar,et al. Slow dynamics and high variability in balanced cortical networks with clustered connections , 2012, Nature Neuroscience.
[62] Wulfram Gerstner,et al. Theory and Simulation in Neuroscience , 2012, Science.
[63] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[64] James G. Scott,et al. Fully Bayesian inference for neural models with negative-binomial spiking , 2012, NIPS.
[65] Joachim Haß,et al. An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data , 2012, Front. Comput. Neurosci..
[66] James G. Scott,et al. Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables , 2012, 1205.0310.
[67] Michael L. Hines,et al. Mitral cell spike synchrony modulated by dendrodendritic synapse location , 2012, Front. Comput. Neurosci..
[68] Mark C. W. van Rossum,et al. Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits , 2013, Front. Comput. Neurosci..
[69] E. Marder,et al. Multiple Mechanisms Switch an Electrically Coupled, Synaptically Inhibited Neuron between Competing Rhythmic Oscillators , 2013, Neuron.
[70] B. Rodríguez,et al. Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology , 2013, Proceedings of the National Academy of Sciences.
[71] David Sussillo,et al. Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks , 2013, Neural Computation.
[72] Mark S. Goldman,et al. A Modeling Framework for Deriving the Structural and Functional Architecture of a Short-Term Memory Microcircuit , 2013, Neuron.
[73] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[74] S. Sisson,et al. A comparative review of dimension reduction methods in approximate Bayesian computation , 2012, 1202.3819.
[75] J. Sethna,et al. Parameter Space Compression Underlies Emergent Theories and Predictive Models , 2013, Science.
[76] O. Sporns. Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.
[77] Hao Huang,et al. Estimating parameters and predicting membrane voltages with conductance-based neuron models , 2014, Biological Cybernetics.
[78] Eve Marder,et al. Many Parameter Sets in a Multicompartment Model Oscillator Are Robust to Temperature Perturbations , 2014, The Journal of Neuroscience.
[79] Max Welling,et al. GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation , 2014, UAI.
[80] Eve Marder,et al. Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model. , 2014, Neuron.
[81] Tobias C. Potjans,et al. The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model , 2012, Cerebral cortex.
[82] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[83] Richard Wilkinson,et al. Accelerating ABC methods using Gaussian processes , 2014, AISTATS.
[84] Sarah Filippi,et al. A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation , 2014, Nature Protocols.
[85] Aidan C. Daly,et al. Hodgkin–Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods , 2015, Royal Society Open Science.
[86] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[87] Romain Brette,et al. What Is the Most Realistic Single-Compartment Model of Spike Initiation? , 2015, PLoS Comput. Biol..
[88] Eve Marder,et al. Computational models in the age of large datasets , 2015, Current Opinion in Neurobiology.
[89] E. Marder,et al. Robust circuit rhythms in small circuits arise from variable circuit components and mechanisms , 2015, Current Opinion in Neurobiology.
[90] Wulfram Gerstner,et al. Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models , 2015, PLoS Comput. Biol..
[91] Daniel B. Rubin,et al. The Stabilized Supralinear Network: A Unifying Circuit Motif Underlying Multi-Input Integration in Sensory Cortex , 2015, Neuron.
[92] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[93] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[94] Allen Cell Types Database , 2016 .
[95] Henry Markram,et al. BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience , 2016, Front. Neuroinform..
[96] Eve Marder,et al. Temperature-Robust Neural Function from Activity-Dependent Ion Channel Regulation , 2016, Current Biology.
[97] Kenneth D Harris,et al. Inhibitory control of correlated intrinsic variability in cortical networks , 2016, bioRxiv.
[98] Iain Murray,et al. Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation , 2016, 1605.06376.
[99] Michael U. Gutmann,et al. Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models , 2015, J. Mach. Learn. Res..
[100] Christof Koch,et al. Generalized leaky integrate-and-fire models classify multiple neuron types , 2017, Nature Communications.
[101] Christof Koch,et al. Systematic generation of biophysically detailed models for diverse cortical neuron types , 2018, Nature Communications.
[102] Prabhat,et al. Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators , 2017, ArXiv.
[103] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[104] Frank D. Wood,et al. Using synthetic data to train neural networks is model-based reasoning , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[105] Rajnish Ranjan,et al. Mapping the function of neuronal ion channels in model and experiment , 2017, eLife.
[106] John P. Cunningham,et al. Maximum Entropy Flow Networks , 2017, ICLR.
[107] Frank D. Wood,et al. Inference Compilation and Universal Probabilistic Programming , 2016, AISTATS.
[108] Jakob H. Macke,et al. Fast amortized inference of neural activity from calcium imaging data with variational autoencoders , 2017, NIPS.
[109] Jakob H. Macke,et al. Flexible statistical inference for mechanistic models of neural dynamics , 2017, NIPS.
[110] J. Gold,et al. On the nature and use of models in network neuroscience , 2018, Nature Reviews Neuroscience.
[111] Yun S. Song,et al. A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks , 2018, bioRxiv.
[112] Bai Jiang,et al. Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network , 2015, 1510.02175.
[113] Jakob H. Macke,et al. Likelihood-free inference with emulator networks , 2018, AABI.
[114] Iain Murray,et al. Sequential Neural Methods for Likelihood-free Inference , 2018, ArXiv.
[115] Ann B. Lee,et al. ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations , 2018, Journal of Computational and Graphical Statistics.
[116] Josef Ladenbauer,et al. Inferring and validating mechanistic models of neural microcircuits based on spike-train data , 2018, Nature Communications.
[117] Chethan Pandarinath,et al. Inferring single-trial neural population dynamics using sequential auto-encoders , 2017, Nature Methods.
[118] Yee Whye Teh,et al. Faithful Inversion of Generative Models for Effective Amortized Inference , 2017, NeurIPS.
[119] R. Baker,et al. Mechanistic models versus machine learning, a fight worth fighting for the biological community? , 2018, Biology Letters.
[120] Kevin Burrage,et al. Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology , 2017, Science Advances.
[121] Maria C. Dadarlat,et al. Flow stimuli reveal ecologically appropriate responses in mouse visual cortex , 2018, Proceedings of the National Academy of Sciences.
[122] Aki Vehtari,et al. Validating Bayesian Inference Algorithms with Simulation-Based Calibration , 2018, 1804.06788.
[123] Eve Marder,et al. Circuit Robustness to Temperature Perturbation Is Altered by Neuromodulators , 2017, Neuron.
[124] Timothy O'Leary,et al. Homeostasis, failure of homeostasis and degenerate ion channel regulation , 2018 .
[125] Eve Marder,et al. Visualization of currents in neural models with similar behavior and different conductance densities , 2019, eLife.
[126] Surya Ganguli,et al. Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics , 2019, NeurIPS.
[127] Kristofer E. Bouchard,et al. Inferring neuronal ionic conductances from membrane potentials using CNNs , 2019, bioRxiv.
[128] Philipp Berens,et al. Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse , 2019, bioRxiv.
[129] Henry Markram,et al. A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family , 2019, Front. Cell. Neurosci..
[130] Sean R. Bittner,et al. Interrogating theoretical models of neural computation with deep inference , 2019, bioRxiv.
[131] David S. Greenberg,et al. Automatic Posterior Transformation for Likelihood-Free Inference , 2019, ICML.
[132] Gilles Louppe,et al. Likelihood-free MCMC with Approximate Likelihood Ratios , 2019, ArXiv.
[133] Michael U. Gutmann,et al. Efficient Bayesian Experimental Design for Implicit Models , 2018, AISTATS.
[134] Iain Murray,et al. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows , 2018, AISTATS.
[135] Edward S. Boyden,et al. Advances in the automation of whole-cell patch clamp technology , 2019, Journal of Neuroscience Methods.
[136] Jonathan Oesterle,et al. Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics , 2020, bioRxiv.
[137] Inference of a Mesoscopic Population Model from Population Spike Trains , 2019, Neural Computation.
[138] Iain Murray,et al. On Contrastive Learning for Likelihood-free Inference , 2020, ICML.
[139] David S. Greenberg,et al. SBI - A toolkit for simulation-based inference , 2020, J. Open Source Softw..
[140] Gilles Louppe,et al. The frontier of simulation-based inference , 2019, Proceedings of the National Academy of Sciences.
[141] Eric Nalisnick,et al. Normalizing Flows for Probabilistic Modeling and Inference , 2019, J. Mach. Learn. Res..