J. Macke

发表

Estimating state and Parameters in state space Models of Spike trains

Lars Buesing, J. Macke, M. Sahani, 2015 .

Learning stable, regularised latent models of neural population dynamics

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

Psignifit 4: Pain-free Bayesian Inference for Psychometric Functions.

Stefan Harmeling, Heiko H. Schütt, Jakob Macke, 2015, Journal of vision.

Appendix to ‘ Quantifying the effect of inter-trial dependence on perceptual decisions ’

J. Macke, Ingo Fründ, Felix Wichmann, 2014 .

Gaussian process methods for estimating cortical maps

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

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.

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

William F. Podlaski, David S. Greenberg, J. Macke, 2020, eLife.

Common input explains higher-order correlations and entropy in a simple model of neural population activity.

M. Bethge, M. Opper, J. Macke, 2011, Physical review letters.

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

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

Inferring decoding strategies from choice probabilities in the presence of correlated variability

M. Bethge, J. Macke, S. Gerwinn, 2013, Nature Neuroscience.

Neural population coding: combining insights from microscopic and mass signals

J. Macke, C. Kayser, S. Panzeri, 2015, Trends in Cognitive Sciences.

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.

Quantifying the effect of intertrial dependence on perceptual decisions.

J. Macke, Ingo Fründ, Felix Wichmann, 2014, Journal of vision.

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Johann S. Hawe, B. Schölkopf, J. Macke, 2014, Nature Biotechnology.

KOGWIS2018: Computational Approaches to Cognitive Science

J. Macke, F. Jäkel, B. Mohler, 2018 .

Dentate gyrus population activity during immobility supports formation of precise memories

Laura A. Ewell, J. Macke, H. Beck, 2020, bioRxiv.

12 78 4 v 1 [ cs . L G ] 2 9 M ay 2 01 9 Intrinsic dimension of data representations in deep neural networks

J. Macke, A. Laio, D. Zoccolan, 2019 .

Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces.

Jakob H Macke, Felix A Wichmann, J. Macke, 2010, Journal of vision.

Choice (-history) correlations in sensory cortex: cause or consequence?

Jakob H Macke, Hendrikje Nienborg, J. Macke, 2019, Current Opinion in Neurobiology.

Low error discrimination using a correlated population code.

Michael J. Berry, Dario Amodei, Jakob Macke, 2012, Journal of neurophysiology.

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.

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

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

Pattern recognition methods in classifying fMRI data

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

Benchmarking Simulation-Based Inference pdf

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

Accuracy of Rats in Discri minating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy Graphical

J. Macke, D. Zoccolan, V. Djurdjević, 2018 .

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

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

Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) – a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

J. Macke, Philipp Berens, M. Erb, 2022, BMC Neurology.

Disparate energy consumption despite similar network activity

J. Macke, P. J. Gonçalves, Michael Deistler, 2021, bioRxiv.

Inference of a Mesoscopic Population Model from Population Spike Trains

J. Macke, A. Longtin, A. René, 2019, Neural Computation.

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

Stephen R. Green, J. Macke, B. Scholkopf, 2022, ArXiv.

Variational methods for simulation-based inference

J. Macke, Michael Deistler, Manuel Glöckler, 2022, ICLR.

Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks

Thomas P. Reber, J. Macke, F. Mormann, 2022, bioRxiv.

Supplementary details and explanations for paper “ A model of neural population activity in the presence of common input explains higher-order correlations and entropy ”

M. Bethge, M. Opper, J. Macke, 2014 .

Estimation of skeletal kinematics in freely moving rodents

Damian J. Wallace, J. Macke, J. Kerr, 2022, Nature Methods.

Truncated proposals for scalable and hassle-free simulation-based inference

J. Macke, P. J. Gonçalves, Michael Deistler, 2022, 2210.04815.

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 .

Adapting to noise distribution shifts in flow-based gravitational-wave inference

Stephen R. Green, B. Schölkopf, J. Macke, 2022, Physical Review D.

Stitching neural activity in space and time: theory and practice

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

Truncated proposals for scalable and hassle-free simulation-based inference

J. Macke, P. J. Gonçalves, Michael Deistler, 2022, NeurIPS.

Efficient identification of informative features in simulation-based inference

J. Macke, Philipp Berens, Michael Deistler, 2022, 2210.11915.

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

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

Flexible and eﬃcient 1 simulation-based inference for 2 models of decision-making 3

J. Macke, Jan-Matthis Lueckmann, Richard Gao, 2022 .

Flexible and efficient simulation-based inference for models of decision-making

J. Macke, Jan-Matthis Lueckmann, Richard Gao, 2021, bioRxiv.

Simulation Intelligence: Towards a New Generation of Scientific Methods pdf

Hector Zenil, Manuela Veloso, Atilim Gunecs Baydin, 2021, ArXiv.

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

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

Automatic Posterior Transformation for Likelihood-Free Inference pdf

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

Likelihood-free inference with emulator networks pdf

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

Flexible statistical inference for mechanistic models of neural dynamics

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

GATSBI: Generative Adversarial Training for Simulation-Based Inference

David S. Greenberg, J. Macke, Álvaro Tejero-Cantero, 2022, ICLR.

Benchmarking Simulation-Based Inference

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

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

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

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

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

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

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

Inducing Metric Violations in Human Similarity Judgements

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

Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series

J. Macke, T. Dijkstra, O. Kohlbacher, 2023, medRxiv.

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

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

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

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

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.

Signatures of criticality arise from random subsampling in simple population models

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

Signatures of criticality arise in simple neural population models with correlations

M. Bethge, J. Macke, Philipp Berens, 2016, 1603.00097.

Can Serial Dependencies in Choices and Neural Activity Explain Choice Probabilities?

Jakob H Macke, Hendrikje Nienborg, Jan-Matthis Lueckmann, 2017, The Journal of Neuroscience.

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.

Comparing neural simulations by neural density estimation

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

Synchronous activity patterns in the dentate gyrus during immobility

Laura A. Ewell, J. Macke, H. Beck, 2021, eLife.

Combined statistical-mechanistic modeling links ion channel genes to physiology of cortical neuron types

J. Macke, Philipp Berens, A. Tolias, 2023, bioRxiv.

Efficient identification of informative features in simulation-based inference

J. Macke, Philipp Berens, Michael Deistler, 2022, NeurIPS.

Energy-efficient network activity from disparate circuit parameters

J. Macke, P. J. Gonçalves, Michael Deistler, 2022, bioRxiv.

Intrinsic dimension of data representations in deep neural networks

J. Macke, A. Laio, D. Zoccolan, 2019, NeurIPS.

Generating Spike Trains with Specified Correlation Coefficients

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

Discrimination of simple objects decoded from the output of retinal ganglion cells upon sinusoidal electrical stimulation

J. Macke, A. Corna, Meng-Jung Lee, 2021, Journal of neural engineering.

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

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

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

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

Electrophysiology Analysis, Bayesian

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

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

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

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

J. Macke, F. Mormann, M. Kehl, 2021, Brain sciences.

The impact of neuron morphology on cortical network architecture

J. Macke, B. Sakmann, C. D. de Kock, 2020, bioRxiv.

Dealing with sequential dependencies in psychophysical data

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

Group equivariant neural posterior estimation

Stephen R. Green, B. Schölkopf, J. Macke, 2021, ICLR.

Real-time gravitational-wave science with neural posterior estimation

Stephen R. Green, B. Schölkopf, J. Macke, 2021, Physical review letters.

Comparison of Pattern Recognition Methods in Classifying High-resolution Bold Signals Obtained at High Magnetic Field in Monkeys

Arthur Gretton, Nikos K Logothetis, Shih-pi Ku, 2008 .

Estimating receptive fields without spike-triggering

M. Bethge, J. Macke, G. Zeck, 2007 .

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Srinivas C. Turaga, J. Macke, Shin-ya Takemura, 2023, bioRxiv.

The effect of pairwise neural correlations on global population statistics

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

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

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

A Theory for the Emergence of Neocortical Network Architecture

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

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

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

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

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

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

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

Flow Matching for Scalable Simulation-Based Inference

Stephen R. Green, B. Schölkopf, J. Macke, 2023, ArXiv.

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

Stephen R. Green, B. Schölkopf, J. Macke, 2022, Physical review letters.

Simulation-based inference for efficient identification of generative models in computational connectomics

J. Macke, M. Oberlaender, H. Hege, 2023, bioRxiv.

Adversarial robustness of amortized Bayesian inference

J. Macke, Michael Deistler, Manuel Glöckler, 2023, ICML.

Anatomically-based skeleton kinetics and pose estimation in freely-moving rodents

Damian J. Wallace, J. Macke, J. Kerr, 2021, bioRxiv.

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

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

Disparate energy consumption despite similar network activity

J. Macke, P. J. Gonçalves, Michael Deistler, 2021 .

Multiscale Metamorphic VAE for 3D Brain MRI Synthesis

Christian F. Baumgartner, J. Macke, J. Kapoor, 2023, ArXiv.

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation

J. Macke, Michael Deistler, Richard Gao, 2023, ArXiv.

Bayesian Inference for Generalized Linear Models for Spiking Neurons

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

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

Srinivas C. Turaga, Timothy A. Machado, Patrick J. Mineault, 2017, bioRxiv.

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

J. Macke, G. Bassetto, P. Putzky, 2014 .

Empirical models of spiking in neural populations

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

Adversarial Training of Neural Encoding Models on Population Spike Trains

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

Supporting Online Material Materials and Methods Som Text Figs. S1 to S9 References the Asynchronous State in Cortical Circuits

P. Dayan, N. Parga, J. Macke, 2022 .

Frontiers in Computational Neuroscience Correlation-based Analysis and Generation of Multiple Spike Trains Using Hawkes Models with an Exogenous Input

Shy Shoham, Michael Krumin, Inna Reutsky, 2022 .

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

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

Low error discrimination using a correlated population code.

Michael J. Berry, Dario Amodei, J. Macke, 2012, Journal of neurophysiology.

Gaussian process methods for estimating cortical maps

M. Bethge, J. Macke, L. White, 2011, NeuroImage.

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

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

Bayesian estimation of orientation preference maps

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

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

J. Macke, P. Putzky, Giacomo Bassetto, 2014, NIPS.

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

J. Macke, O. Barak, Matthijs Pals, 2024, bioRxiv.

Simultaneous identification of models and parameters of scientific simulators

J. Macke, Cornelius Schröder, 2023, ArXiv.

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

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

Generating realistic neurophysiological time series with denoising diffusion probabilistic models

J. Macke, Richard Gao, Julius Vetter, 2023, bioRxiv.

Flow Matching for Scalable Simulation-Based Inference

Stephen R. Green, J. Macke, Maximilian Dax, 2023, NeurIPS.

Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers

J. Macke, R. Drews, V. Višnjević, 2023, ArXiv.

Supporting Online Material Materials and Methods Som Text Figs. S1 to S9 References the Asynchronous State in Cortical Circuits

P. Dayan, N. Parga, J. Macke, 2022 .

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations

J. Macke, Michael Deistler, Nathanael Bosch, 2024, ArXiv.

Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation

J. Macke, Richard Gao, Julius Vetter, 2024, arXiv.org.

Neuronal decision-making with realistic spiking models

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