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Computer Science
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2026

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Best Scientists

D-Index
177
Citations
166248
World Ranking
663
National Ranking
416

Neuroscience

D-Index
179
Citations
163455
World Ranking
55
National Ranking
35

Computer Science

D-Index
136
Citations
124213
World Ranking
81
National Ranking
50

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2026 - Research.com Neuroscience in United States Leader Award
  • 2025 - Research.com Best Scientists Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Neuroscience in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2017 - Fellow, National Academy of Inventors
  • 2014 - Fellow of American Physical Society (APS) Citation For pioneering work in computational biological physics towards understanding the structure and function of correlations in large scale biological systems, including representation of memories in the brain, protein sequences, and statistical learning algorithms
  • 2013 - IEEE Frank Rosenblatt Award
  • 2013 - Fellow of the American Academy of Arts and Sciences
  • 2011 - Member of the National Academy of Engineering For contributions to artificial and real neural network algorithms and applying signal processing models to neuroscience.
  • 2010 - Member of the National Academy of Sciences
  • 2008 - Member of the National Academy of Medicine (NAM)
  • 2006 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2002 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society
  • 2000 - IEEE Fellow For fundamental advances in the theory and practice of neural networks and for contributions to computational neuroscience.

Overview

Terrence J. Sejnowski is affiliated with the Salk Institute for Biological Studies in the United States. Their research primarily spans the field of neuroscience, with a focus on subfields such as cognitive neuroscience, cellular and molecular neuroscience, molecular biology, electrical and electronic engineering, and artificial intelligence.

The scientist has contributed extensively to topics including neural dynamics and brain function, advanced memory and neural computing, neuroscience and neuropharmacology research, EEG and brain-computer interfaces, functional brain connectivity studies, photoreceptor and optogenetics research, and neural networks and applications.

Terrence J. Sejnowski has a number of frequently collaborating co-authors, including Thomas M. Bartol, Claudia Lainscsek, Lyle Muller, Hava T. Siegelmann, and Padmini Rangamani.

Their publication record includes contributions to several frequent venues, among them:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Proceedings of the National Academy of Sciences
  • UNC Libraries
  • Neural Computation

Among recent papers authored or co-authored by Sejnowski are:

  • The unreasonable effectiveness of deep learning in artificial intelligence (2020), Proceedings of the National Academy of Sciences
  • Spontaneous travelling cortical waves gate perception in behaving primates (2020), Nature
  • Catalyzing next-generation Artificial Intelligence through NeuroAI (2023), Nature Communications
  • Biological underpinnings for lifelong learning machines (2022), Nature Machine Intelligence
  • The Mind of a Mouse (2020), Cell

Over the course of their career, Terrence J. Sejnowski has received several awards and honors. These include being named Fellow of the National Academy of Inventors in 2017 and Fellow of the American Physical Society in 2014, cited for pioneering work in computational biological physics. They were also the recipient of the IEEE Frank Rosenblatt Award in 2013 and have been recognized as a Fellow of the American Academy of Arts and Sciences the same year.

Sejnowski was elected to the National Academy of Engineering in 2011 for contributions to artificial and real neural network algorithms and for applying signal processing models to neuroscience. Additionally, they are a member of the National Academy of Sciences (2010) and the National Academy of Medicine (2008). Multiple fellowships with scientific associations, including the American Association for the Advancement of Science in 2006 and IEEE Fellow status in 2000, further mark their career.

Best Publications

  • An information-maximization approach to blind separation and blind deconvolution

    Anthony J. Bell;Terrence J. Sejnowski

  • A learning algorithm for Boltzmann machines

    David H. Ackley;Geoffrey E. Hinton;Terrence J. Sejnowski

  • Thalamocortical oscillations in the sleeping and aroused brain

    Mircea Steriade;David A. McCormick;Terrence J. Sejnowski

  • Running enhances neurogenesis, learning, and long-term potentiation in mice

    H M van Praag;B R Christie;T J Sejnowski;F H Gage

  • Removing electroencephalographic artifacts by blind source separation.

    Tzyy-Ping Jung;Tzyy-Ping Jung;Scott Makeig;Colin Humphries;Te-Won Lee;Te-Won Lee

  • The Computational Brain

    Patricia Smith Churchland;Terrence J. Sejnowski

  • The "independent components" of natural scenes are edge filters.

    Anthony J. Bell;Terrence J. Sejnowski

  • Face recognition by independent component analysis

    M.S. Bartlett;J.R. Movellan;T.J. Sejnowski

  • Independent Component Analysis of Electroencephalographic Data

    Scott Makeig;Anthony J. Bell;Tzyy-Ping Jung;Terrence J. Sejnowski

  • Parallel Networks that Learn to Pronounce English Text

    T. J. Sejnowski

  • Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources

    Te-Won Lee;Mark Girolami;Terrence J. Sejnowski;Terrence J. Sejnowski

  • A framework for mesencephalic dopamine systems based on predictive Hebbian learning

    PR Montague;P Dayan;TJ Sejnowski

  • Analysis of fMRI data by blind separation into independent spatial components

    Martin J. Mckeown;Scott Makeig;Greg G. Brown;Tzyy-Ping Jung

  • Reliability of spike timing in neocortical neurons

    Zachary F. Mainen;Terrence J. Sejnowski;Terrence J. Sejnowski

  • Learning and relearning in Boltzmann machines

    G. E. Hinton;T. J. Sejnowski

  • Dynamic Brain Sources of Visual Evoked Responses

    S. Makeig;M. Westerfield;T.-P. Jung;S. Enghoff

  • Global Epigenomic Reconfiguration During Mammalian Brain Development

    Ryan Lister;Ryan Lister;Eran A. Mukamel;Joseph R. Nery;Mark Urich

  • Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

    Arnaud Delorme;Terrence J. Sejnowski;Scott Makeig

  • Slow feature analysis: unsupervised learning of invariances

    Laurenz Wiskott;Terrence J. Sejnowski

  • Predicting the secondary structure of globular proteins using neural network models.

    Ning Qian;Terrence J. Sejnowski

Frequent Co-Authors

Maxim Bazhenov
Maxim Bazhenov University of California, San Diego
Te-Won Lee
Te-Won Lee Qualcomm (United States)
Tzyy-Ping Jung
Tzyy-Ping Jung University of California, San Diego
Scott Makeig
Scott Makeig University of California, San Diego
Jean-Marc Fellous
Jean-Marc Fellous University of Arizona
Paul H. E. Tiesinga
Paul H. E. Tiesinga Radboud University
Igor Timofeev
Igor Timofeev Université Laval
Alain Destexhe
Alain Destexhe Centre national de la recherche scientifique, CNRS
Mircea Steriade
Mircea Steriade Université Laval
Robert N. Weinreb
Robert N. Weinreb University of California, San Diego

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