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Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience.
A quantitative description of membrane current and its application to conduction and excitation in nerve
A L Hodgkin and A F Huxley · 1952
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Monte carlo methods of inference for implicit statistical models
P J Diggle and R J Gratton · 1984
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Keeping the neural networks simple by minimizing the description length of the weights
G E Hinton and D Van Camp · 1993
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Chaos in neuronal networks with balanced excitatory and inhibitory activity
C van Vreeswijk and H Sompolinsky · 1996
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Nonparametric input estimation in physiological systems: problems, methods, and case studies
G De Nicolao, G Sparacino, and C Cobelli · 1997
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Population growth of human y chromosomes: a study of y chromosome microsatellites
J K Pritchard, M T Seielstad, A Perez-Lezaun, and M W Feldman · 1999
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Approximate bayesian computation in population genetics
M Beaumont, W Zhang, and D J Balding · 2002
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Alternative to hand-tuning conductance-based models: Construction and analysis of databases of model neurons
A A Prinz, C P Billimoria, and E Marder · 2003
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Markov chain monte carlo without likelihoods
P Marjoram, J Molitor, V Plagnol, and S Tavare · 2003
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A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data
S Druckmann, Y Banitt, A Gidon, F Schürmann, H Markram, and I Segev · 2007
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Spatio-temporal correlations and visual signalling in a complete neuronal population
J W Pillow, J Shlens, L Paninski, A Sher, A M Litke, E J Chichilnisky, and E P Simoncelli · 2008
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Minimal hodgkin-huxley type models for different classes of cortical and thalamic neurons
M Pospischil, M Toledo-Rodriguez, C Monier, Z Piwkowska, T Bal, Y Frégnac, H Markram, and A Destexhe · 2008
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Smoothing of, and parameter estimation from, noisy biophysical recordings
Q J M Huys and L Paninski · 2009
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The NEURON Book
N T Carnevale and M L Hines · 2009
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Adaptive approximate bayesian computation
M A Beaumont, J Cornuet, J Marin, and C P Robert · 2009
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Non-linear regression models for approximate bayesian computation
M G B Blum and O François · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
S N Wood · 2010
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A sequential monte carlo approach to estimate biophysical neural models from spikes
L Meng, M A Kramer, and U T Eden · 2011
Cited alongside, same era.
Fitting neuron models to spike trains
C Rossant, D F M Goodman, B Fontaine, J Platkiewicz, A K Magnusson, and R Brette · 2011
Cited alongside, same era.
Statistical inference for stochastic simulation models–theory and application
F Hartig, J M Calabrese, B Reineking, T Wiegand, and A Huth · 2011
Cited alongside, same era.
Practical variational inference for neural networks
A Graves · 2011
Cited alongside, same era.
Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties
E Hay, S Hill, F Schürmann, H Markram, and I Segev · 2011
Cited alongside, same era.
Approximate bayesian computation via regression density estimation
Y Fan, D J Nott, and S A Sisson · 2013
Reconstruction and Simulation of Neocortical Microcircuitry
H Markram et al · 2015
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Hodgkin–huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate bayesian methods
Aidan C Daly, David J Gavaghan, Chris Holmes, and Jonathan Cooper · 2015
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E Meeds, R Leenders, and M Welling · 2015
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Sequential monte carlo with adaptive weights for approximate bayesian computation
F V Bonassi, M West, et al · 2015
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Neural adaptive sequential monte carlo
D P Kingma, T Salimans, and M Welling · 2015
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Learning summary statistic for approximate bayesian computation via deep neural network
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Cited alongside, same era.
A comparative review of dimension reduction methods in approximate bayesian computation
M G B Blum, M A Nunes, Ds Prangle, S A Sisson, et al · 2013
Cited alongside, same era.
Bayesian inference for logistic models using pólya–gamma latent variables
N G Polson, J G Scott, and J Windle · 2013
Cited alongside, same era.
Neuronal dynamics: From single neurons to networks and models of cognition
W Gerstner, W M Kistler, R Naud, and L Paninski · 2014
Cited alongside, same era.
Estimating parameters and predicting membrane voltages with conductance-based neuron models
C D Meliza, M Kostuk, H Huang, A Nogaret, D Margoliash, and H D I Abarbanel · 2014
Cited alongside, same era.
An efficient automated parameter tuning framework for spiking neural networks
Kristofor D Carlson, Jayram Moorkanikara Nageswaran, Nikil Dutt, and Jeffrey L Krichmar · 2014
Cited alongside, same era.
A flexible, interactive software tool for fitting the parameters of neuronal models
P Friedrich, M Vella, A I Gulyás, T F Freund, and S Káli · 2014
Cited alongside, same era.
B Jiang, T Wu, Cs Zheng, and W H Wong · 2015
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Neural adaptive sequential monte carlo
S Gu, Z Ghahramani, and R E Turner · 2015
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Bluepyopt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience
W Van Geit, M Gevaert, G Chindemi, C Rössert, J Courcol, E B Muller, F Schürmann, I Segev, and H Markram · 2016
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Inhibitory control of correlated intrinsic variability in cortical networks
C Stringer, M Pachitariu, N A Steinmetz, M Okun, P Bartho, K D Harris, M Sahani, and N A Lesica · 2016
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Fundamentals and recent developments in approximate bayesian computation
J Lintusaari, M U Gutmann, R Dutta, S Kaski, and J Corander · 2016
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Variational bayes with synthetic likelihood
V M H Ong, D J Nott, M Tran, S A Sisson, and C C Drovandi · 2016
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Bayesian latent structure discovery from multi-neuron recordings
S Linderman, R P Adams, and J W Pillow · 2016
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Fast epsilon-free inference of simulation models with bayesian conditional density estimation
G Papamakarios and I Murray · 2017
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Bayesian synthetic likelihood
L F Price, C C Drovandi, A Lee, and David J N · 2017
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On the stability and dynamics of stochastic spiking neuron models: Nonlinear hawkes process and point process glms
F Gerhard, M Deger, and W Truccolo · 2017
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Efficient acquisition rules for model-based approximate bayesian computation
Marko Järvenpää, Michael U Gutmann, Aki Vehtari, and Pekka Marttinen · 2017
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Using computational theory to constrain statistical models of neural data
S W Linderman and S J Gershman · 2017
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