World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5071
World Ranking
11724
National Ranking
577

Overview

Jakob H. Macke is affiliated with the Max Planck Institute for Intelligent Systems in Germany. Their research spans multiple disciplines, primarily focusing on neuroscience and computer science.

The main fields of study include:

  • Neuroscience
  • Computer Science

Within these fields, their work concentrates on the following subfields:

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Cellular and Molecular Neuroscience
  • Astronomy and Astrophysics
  • Molecular Biology

The core topics that characterize this scientist's research are:

  • Neural dynamics and brain function
  • Functional Brain Connectivity Studies
  • Gaussian Processes and Bayesian Inference
  • Neural Networks and Applications
  • Pulsars and Gravitational Waves Research
  • EEG and Brain-Computer Interfaces
  • Advanced Memory and Neural Computing

Jakob H. Macke has contributed to scientific literature through publications in the following venues:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature Methods
  • eLife

Notable recent papers include:

  • Training deep neural density estimators to identify mechanistic models of neural dynamics, 2020, eLife
  • Deep learning enables fast and dense single-molecule localization with high accuracy, 2021, Nature Methods
  • Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference, 2023, Physical Review Letters
  • The impact of neuron morphology on cortical network architecture, 2022, Cell Reports
  • Simulation Intelligence: Towards a New Generation of Scientific Methods, 2021, arXiv (Cornell University)

Their collaborative work is marked by repeated partnerships with several frequent co-authors:

  • Michael Deistler
  • Pedro J. Gonçalves
  • Richard Gao
  • Julius Vetter
  • Cornelius Schröder

Best Publications

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

    Heiko H. Schütt;Heiko H. Schütt;Stefan Harmeling;Jakob H. Macke;Jakob H. Macke;Felix A. Wichmann

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

    Pedro J Gonçalves;Pedro J Gonçalves;Jan-Matthis Lueckmann;Jan-Matthis Lueckmann;Michael Deistler;Michael Deistler;Marcel Nonnenmacher;Marcel Nonnenmacher

  • Neural population coding: combining insights from microscopic and mass signals

    Stefano Panzeri;Stefano Panzeri;Jakob H. Macke;Jakob H. Macke;Joachim Gross;Christoph Kayser

  • Quantifying the effect of intertrial dependence on perceptual decisions.

    Ingo Fründ;Ingo Fründ;Felix A. Wichmann;Jakob H. Macke

  • Generating spike trains with specified correlation coefficients

    Jakob H. Macke;Philipp Berens;Alexander S. Ecker;Andreas S. Tolias

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

    Artur Speiser;Lucas-Raphael Müller;Philipp Hoess;Ulf Matti

  • Empirical models of spiking in neural populations

    Jakob H Macke;Lars Buesing;John P Cunningham;Byron M Yu

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

    Robert Küffner;Neta Zach;Raquel Norel;Johann Hawe

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

    Philipp Berens;Jeremy Freeman;Thomas Deneux;Nikolay Chenkov

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

    Ralf M Haefner;Sebastian Gerwinn;Jakob H Macke;Matthias Bethge

  • sbi: A toolkit for simulation-based inference

    Álvaro Tejero-Cantero;Jan Boelts;Michael Deistler;Jan-Matthis Lueckmann

  • Intrinsic dimension of data representations in deep neural networks

    Alessio Ansuini;Alessandro Laio;Jakob H. Macke;Davide Zoccolan

  • Flexible statistical inference for mechanistic models of neural dynamics

    Jan-Matthis Lueckmann;Pedro J. Goncalves;Giacomo Bassetto;Kaan Öcal

  • Intrinsic dimension of data representations in deep neural networks

    Alessio Ansuini;Alessandro Laio;Jakob H. Macke;Davide Zoccolan

  • Automatic Posterior Transformation for Likelihood-Free Inference

    David S. Greenberg;Marcel Nonnenmacher;Jakob H. Macke

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

    David G. T. Barrett;Ari S. Morcos;Jakob H. Macke

  • Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity

    Jakob H. Macke;Manfred Opper;Matthias Bethge

  • Flexible statistical inference for mechanistic models of neural dynamics

    Jan-Matthis Lueckmann;Pedro J. Goncalves;Giacomo Bassetto;Kaan Öcal

  • Bayesian inference for generalized linear models for spiking neurons

    Sebastian Gerwinn;Sebastian Gerwinn;Jakob H. Macke;Jakob H. Macke;Jakob H. Macke;Matthias Bethge;Matthias Bethge

  • Contour−propagation algorithms for semi−automated reconstruction of neural processes

    Jakob H. Macke;Nina Maack;Rocky Gupta;Winfried Denk

  • Likelihood-free inference with emulator networks

    Jan-Matthis Lueckmann;Giacomo Bassetto;Theofanis Karaletsos;Jakob H. Macke

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

    Lars Buesing;Jakob H Macke;Maneesh Sahani

  • Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys

    Shih pi Ku;Arthur Gretton;Jakob Macke;Nikos K. Logothetis

Frequent Co-Authors

Matthias Bethge
Matthias Bethge University of Tübingen
Felix A. Wichmann
Felix A. Wichmann University of Tübingen
Andreas S. Tolias
Andreas S. Tolias Baylor College of Medicine
Maneesh Sahani
Maneesh Sahani University College London
Manfred Opper
Manfred Opper Technical University of Berlin
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Nikos K. Logothetis
Nikos K. Logothetis Chinese Academy of Sciences
Hans-Christian Hege
Hans-Christian Hege Zuse Institute Berlin
Iain Murray
Iain Murray University of Edinburgh
Jonathan W. Pillow
Jonathan W. Pillow Princeton University

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