World's Best Scientists 2026 revealed!

D-Index & Metrics

Neuroscience

D-Index
79
Citations
25530
World Ranking
1669
National Ranking
817

Engineering and Technology

D-Index
74
Citations
24431
World Ranking
796
National Ranking
276

Overview

Konrad P. Kording is affiliated with the University of Pennsylvania in the United States. Their research primarily spans the field of Neuroscience, with a total of 80 publications in this domain. Within Neuroscience, their subfields of focus include Cognitive Neuroscience, Artificial Intelligence, Molecular Biology, Electrical and Electronic Engineering, and Experimental and Cognitive Psychology.

The scientist's work covers various specialized topics, including:

  • Neural dynamics and brain function
  • Functional Brain Connectivity Studies
  • Advanced Memory and Neural Computing
  • Neural Networks and Applications
  • EEG and Brain-Computer Interfaces
  • Domain Adaptation and Few-Shot Learning
  • Visual perception and processing mechanisms

Kording has contributed to several recent papers, which include:

  • "Tackling Climate Change with Machine Learning," 2022, OPUS 4 (Zuse Institute Berlin)
  • "Causal mapping of human brain function," 2022, Nature Reviews. Neuroscience
  • "Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets," 2020, Nature Communications
  • "Catalyzing next-generation Artificial Intelligence through NeuroAI," 2023, Nature Communications
  • "The neuroconnectionist research programme," 2023, Nature Reviews. Neuroscience

Their frequent co-authors are Jordan Matelsky, Ari S. Benjamin, Titipat Achakulvisut, Lyle Ungar, and Eva L. Dyer, with collaboration counts ranging from 8 to 11 joint publications each.

Publications are often found in several notable venues. Kording has published extensively in arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Trends in Cognitive Sciences, Nature Communications, and PLoS ONE.

Best Publications

  • Bayesian integration in sensorimotor learning

    Konrad P. Körding;Daniel M. Wolpert

  • Causal inference in multisensory perception.

    Konrad P. Körding;Ulrik Beierholm;Wei Ji Ma;Steven Quartz

  • Bayesian decision theory in sensorimotor control.

    Konrad P. Körding;Daniel M. Wolpert

  • A deep learning framework for neuroscience

    Blake A Richards;Timothy P Lillicrap;Philippe Beaudoin;Yoshua Bengio;Yoshua Bengio

  • Toward an Integration of Deep Learning and Neuroscience.

    Adam H. Marblestone;Greg Wayne;Konrad P. Kording

  • Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

    Sohrab Saeb;Mi Zhang;Christopher J Karr;Stephen M Schueller

  • How advances in neural recording affect data analysis

    Ian H Stevenson;Konrad P Kording;Konrad P Kording

  • Over my fake body: body ownership illusions for studying the multisensory basis of own-body perception

    Konstantina Kilteni;Antonella Maselli;Konrad P. Kording;Konrad P. Kording;Mel Slater

  • The dynamics of memory as a consequence of optimal adaptation to a changing body

    Konrad Paul Kording;Joshua B. Tenenbaum;Reza Shadmehr

  • Relevance of Error : What Drives Motor Adaptation?

    Kunlin Wei;Konrad P. Kording

  • Decision Theory: What "Should" the Nervous System Do?

    Konrad Körding

  • Estimating the sources of motor errors for adaptation and generalization.

    Max Berniker;Konrad Kording

  • Could a Neuroscientist Understand a Microprocessor

    Eric Jonas;Konrad Paul Kording

  • The statistics of natural hand movements

    James N. Ingram;Konrad P. Körding;Ian S. Howard;Daniel M. Wolpert

  • Physical principles for scalable neural recording

    Adam Henry Marblestone;Bradley M Zamft;Yael G Maguire;Mikhail G Shapiro

  • The relationship between mobile phone location sensor data and depressive symptom severity.

    Sohrab Saeb;Emily G. Lattie;Stephen M. Schueller;Konrad P. Kording

  • Towards an integration of deep learning and neuroscience

    Adam Marblestone;Greg Wayne;Konrad Kording

  • Catalyzing next-generation Artificial Intelligence through NeuroAI

    Unknown

  • Multisensory perception: from integration to remapping

    Julia Trommershäuser;Konrad P. Körding;Michael S. Landy

  • Bayesian models: the structure of the world, uncertainty, behavior, and the brain

    Iris Vilares;Iris Vilares;Konrad Kording

  • Tackling Climate Change with Machine Learning

    David Rolnick;Priya L. Donti;Lynn H. Kaack;Kelly Kochanski

Frequent Co-Authors

Lee E. Miller
Lee E. Miller Northwestern University
George M. Church
George M. Church Harvard University
Peter König
Peter König Osnabrück University
David C. Mohr
David C. Mohr Northwestern University
Christoph Kayser
Christoph Kayser Bielefeld University
Daniel M. Wolpert
Daniel M. Wolpert Columbia University
Eric J. Perreault
Eric J. Perreault Northwestern University
Paul Schrater
Paul Schrater University of Minnesota
Levi J. Hargrove
Levi J. Hargrove Northwestern University

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