Jakob H. Macke

发表

Teaching deep neural networks to localize single molecules for super-resolution microscopy

Artur Speiser, Lucas-Raphael Müller, Ulf Matti, 2019 .

Analysis of Pattern Recognition Methods in Classifying Bold Signals in Monkeys at 7-Tesla

Arthur Gretton, Nikos K. Logothetis, Jakob H. Macke, 2008 .

Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations

Jakob H. Macke, Srinivas C. Turaga, Marcel Nonnenmacher, 2017, NIPS.

Contour-propagation algorithms for semi-automated reconstruction of neural processes

Alexander Borst, Jakob H. Macke, Winfried Denk, 2008, Journal of Neuroscience Methods.

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data

Maneesh Sahani, Jakob H. Macke, Lars Buesing, 2012, NIPS.

Stitching neural activity in space and time: theory and practice

Jakob H. Macke, Srinivas C. Turaga, Lars Buesing, 2016 .

Estimation Bias in Maximum Entropy Models

Peter E. Latham, Iain Murray, Jakob H. Macke, 2013, Entropy.

Using Bayesian inference to estimate receptive fields from a small number of spikes

Jakob H. Macke, Giacomo Bassetto, 2017 .

Robust estimation for neural state-space models

Jakob H. Macke, Lars Buesing, M Sahani, 2013 .

Low Dimensional Dynamical Models of Neural Populations with Common Input

Jakob H. Macke, Jonathan W. Pillow, Evan Archer, 2014 .

Receptive Fields without Spike-Triggering

Matthias Bethge, Jakob H. Macke, Guenther Zeck, 2007, NIPS.

How biased are maximum entropy models?

Peter E. Latham, Iain Murray, Jakob H. Macke, 2011, NIPS.

The role of stimulus correlations for population decoding in the retina

Michael J. Berry, Jakob H. Macke, G Schwartz, 2008 .

Inferring neural population dynamics from multiple partial measurements of the same circuit

Jakob H. Macke, 2013 .

Statistical Modelling of Psychophysical Data

Jakob H. Macke, 2013 .

Community-based benchmarking improves spike inference from two-photon calcium imaging data

Matthias Bethge, Andreas S. Tolias, Jakob H. Macke, 2017, bioRxiv.

Amortised inference for mechanistic models of neural dynamics

Jakob H. Macke, Chaitanya Chintaluri, Giacomo Bassetto, 2019 .

Low-dimensional dynamical neural population models with shared stimulus drive

Jakob H. Macke, Jonathan W. Pillow, Evan Archer, 2014 .

Bayesian Inference for Generalized Linear Models for Spiking Neurons

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2010, Front. Comput. Neurosci..

Hierarchical models for neural population dynamics in the presence of non-stationarity

Jakob H. Macke, Mijung Park, 2015 .

Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains

Alexander S. Ecker, Matthias Bethge, Jakob H. Macke, 2018, NIPS 2018.

Low-dimensional models of neural population activity in sensory cortical circuits

Jakob H. Macke, Jonathan W. Pillow, Urs Köster, 2014, NIPS.

Benchmarking Simulation-Based Inference pdf

David S. Greenberg, Jan Boelts, Jakob H. Macke, 2021, AISTATS.

Full Bayesian inference for model-based receptive field estimation, with application to primary visual cortex

Jakob H. Macke, Giacomo Bassetto, 2016 .

Dealing with sequential dependencies in psychophysical data

Felix A. Wichmann, Jakob H. Macke, Ingo Fründ, 2012 .

Flexible Bayesian inference for complex models of single neurons

Jakob H. Macke, Pedro J. Gonçalves, Giacomo Bassetto, 2017 .

Automatic Posterior Transformation for Likelihood-Free Inference pdf

David S. Greenberg, Jakob H. Macke, Marcel Nonnenmacher, 2019, ICML.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

Kenneth D. Harris, Matthias Bethge, Emmanouil Froudarakis, 2018, PLoS Comput. Biol..

Adversarial Training of Neural Encoding Models on Population Spike Trains

Jakob H. Macke, Poornima Ramesh, Mohamad Atayi, 2019, 2019 Conference on Cognitive Computational Neuroscience.

Neuronal decision-making with realistic spiking models

Matthias Bethge, Jakob H. Macke, Ralf M. Haefner, 2009 .

Unlocking neural population non-stationarities using hierarchical dynamics models

Jakob H. Macke, Mijung Park, Gergo Bohner, 2015, NIPS.

Bayesian Population Decoding of Spiking Neurons

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2009, Frontiers Comput. Neurosci..

Generating Spike Trains with Specified Correlation Coefficients

Alexander S. Ecker, Matthias Bethge, Philipp Berens, 2009, Neural Computation.

Electrophysiology Analysis, Bayesian

Jakob H. Macke, J. Macke, 2014, Encyclopedia of Computational Neuroscience.

Bayesian Population Decoding of Spiking Neurons

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2009 .

Reconstructing Stimuli from the Spike Times of Leaky Integrate and Fire Neurons

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2010, Front. Neurosci..

Author response: Training deep neural density estimators to identify mechanistic models of neural dynamics

Jakob H. Macke, Pedro J. Gonçalves, Giacomo Bassetto, 2020 .

Training deep neural density estimators to identify mechanistic models of neural dynamics

David S. Greenberg, Jakob H. Macke, Michael Deistler, 2019, bioRxiv.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

Simona Cocco, Rémi Monasson, Robert Chen, 2017, BMC Neuroscience.

Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning pdf

Jakob H. Macke, Srinivas C. Turaga, Artur Speiser, 2019, ArXiv.

Deep learning enables fast and dense single-molecule localization with high accuracy

Lucas-Raphael Müller, Ulf Matti, Christopher J. Obara, 2020, Nature Methods.

A Theory for the Emergence of Neocortical Network Architecture

Hans-Christian Hege, Jakob H. Macke, Marcel Oberlaender, 2020 .

Flexible Models for Population Spike Trains

Alexander S. Ecker, Matthias Bethge, Philipp Berens, 2008 .

Correlations and signatures of criticality in neural population models

Matthias Bethge, Jakob H. Macke, Philipp Berens, 2015, NIPS 2015.

Likelihood-free inference with emulator networks pdf

Jakob H. Macke, Theofanis Karaletsos, Giacomo Bassetto, 2018, AABI.

Using sequential dependencies in neural activity and behavior to dissect choice related activity in V2

Jakob H. Macke, Hendrikje Nienborg, J. Macke, 2014 .

Amortized inference for fast spike prediction from calcium imaging data

Jakob H. Macke, Srinivas C. Turaga, Artur Speiser, 2017 .

Inferring decoding strategy from choice probabilities in the presence of noise correlations

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2012 .

Pattern recognition methods in classifying fMRI data

Arthur Gretton, Nikos K. Logothetis, Jakob H. Macke, 2008 .

Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

Jakob H. Macke, Ari S. Morcos, David G. T. Barrett, 2018, Current Opinion in Neurobiology.

Flexible statistical inference for mechanistic models of neural dynamics

Jakob H. Macke, Marcel Nonnenmacher, Giacomo Bassetto, 2017, NIPS.

Inference of a Mesoscopic Population Model from Population Spike Trains

André Longtin, Jakob H. Macke, Alexandre René, 2020, Neural Computation.

Frontiers in Computational Neuroscience

Jakob H. Macke, Dmitry R. Lyamzin, Nicholas A. Lesica, 2022 .

Empirical models of spiking in neural populations

John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, 2011, NIPS.

3D Pose Recovery in Freely Moving Rats

Klaus Scheffler, Florian Franzen, Jakob H. Macke, 2018 .

Bayesian estimation of orientation preference maps

Matthias Bethge, Matthias Kaschube, Jakob H. Macke, 2009, NIPS.

Nonlinear Receptive Field Analysis: Making Kernel Methods Interpretable

Bernhard Schölkopf, Felix A. Wichmann, Jakob H. Macke, 2007 .

Unsupervised learning of a steerable basis for invariant image representations

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2007, Electronic Imaging.

Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

Felix A. Wichmann, Jakob H. Macke, Stefan Harmeling, 2016, Vision Research.

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Matthias Bethge, Matthias W. Seeger, Jakob H. Macke, 2007, NIPS.

Flexible Bayesian inference for mechanistic models of neural dynamics

Jakob H. Macke, M Nonnenmacher, Pedro J. Goncalves, 2017 .

Toolbox for inference in generalized linear models of spiking neurons

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 2010 .

Supplement for : A Bayesian model for identifying hierarchically organised states in neural population activity

Florian Franzen, Patrick Putzky, Jakob H. Macke, 2014 .

Implications of correlated neuronal noise in decision making circuits for physiology and behavior

Matthias Bethge, Jakob H. Macke, Sebastian Gerwinn, 1900 .

Gaussian process methods for estimating cortical maps

Matthias Bethge, Matthias Kaschube, Jakob H. Macke, 2011, NeuroImage.

Two-photon imaging of neuronal populations in the primary visual cortex representation of the overhead visual field

Benedict Shien Wei Ng, Jakob H. Macke, Jason N. D. Kerr, 2015 .

Characterizing Retinal Ganglion Cell Responses to Electrical Stimulation Using Generalized Linear Models

Jakob H. Macke, Giacomo Bassetto, Sudarshan Sekhar, 2020, Frontiers in Neuroscience.

Multi-Task Generalization and Adaptation between Noisy Digit Datasets: An Empirical Study

Alexander S. Ecker, Matthias Bethge, Jakob H. Macke, 2018 .

Estimating cortical maps with Gaussian process models

Matthias Bethge, Jakob H. Macke, White Le Kaschube M Sebastian G, 2010 .

Unlocking neural population non-stationarity using a hierarchical dynamics model

Jakob H. Macke, Mijung Park, Gergo Bohner, 2015, NIPS 2015.

Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data

Matthias Bethge, Philipp Berens, Jakob H. Macke, 2011, Front. Comput. Neurosci..

Bayesian inference for synaptic connectivity rules in anatomically realistic cortical connectomes

Jan Boelts, Hans-Christian Hege, Jakob H. Macke, 2019 .

Inferring neural population dynamics from multiple partial recordings of the same neural circuit

Adam M. Packer, Jakob H. Macke, Srinivas C. Turaga, 2013, NIPS.

Inducing Metric Violations in Human Similarity Judgements

Klaus-Robert Müller, Felix A. Wichmann, Jakob H. Macke, 2006, NIPS.

Fast amortized inference of neural activity from calcium imaging data with variational autoencoders

Jakob H. Macke, Srinivas C. Turaga, Jinyao Yan, 2017, NIPS.

Intrinsic dimension of data representations in deep neural networks

Alessandro Laio, Jakob H. Macke, Davide Zoccolan, 2019, NeurIPS.

Signatures of criticality arise from random subsampling in simple population models

Matthias Bethge, Philipp Berens, Jakob H. Macke, 2016, PLoS Comput. Biol..

Inferring interactions between cell types from multiple calcium imaging snapshots of the same neural circuit

Jakob H. Macke, Srinivas C. Turaga, Lars Buesing, 2013 .

Benchmarking Simulation-Based Inference

Jan Boelts, Jakob H. Macke, Pedro J. Gonçalves, 2021, AISTATS.

Group equivariant neural posterior estimation

Jakob H. Macke, Michael Deistler, Bernhard Scholkopf, 2021, International Conference on Learning Representations.

Real-time gravitational-wave science with neural posterior estimation pdf

Jakob H. Macke, Bernhard Scholkopf, Maximilian Dax, 2021, Physical Review Letters.

The effect of pairwise neural correlations on global population statistics

Matthias Bethge, Manfred Opper, Jakob H. Macke, 2009 .

Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy

Jakob H. Macke, Daniele Bertolini, Davide Zoccolan, 2018, Current Biology.

Temporal Jitter of the BOLD Signal Reveals a Reliable Initial Dip and Improved Spatial Resolution

Andreas Bartels, Nikos K. Logothetis, Yusuke Murayama, 2013, Current Biology.

Comparing neural simulations by neural density estimation

Jan Boelts, Jakob H. Macke, Henning Sprekeler, 2019, 2019 Conference on Cognitive Computational Neuroscience.