D-Index & Metrics Best Publications
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D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Neuroscience D-index 167 Citations 115,885 624 World Ranking 19 National Ranking 12
Best Scientists D-index 195 Citations 188,820 896 World Ranking 232 National Ranking 157

Research.com Recognitions

Awards & Achievements

2022 - Research.com Best Scientist 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 - Fellow of the American Academy of Arts and Sciences

2013 - IEEE Frank Rosenblatt Award

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)

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

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Neuroscience
  • Statistics

Neuroscience, Artificial intelligence, Independent component analysis, Pattern recognition and Electroencephalography are his primary areas of study. His biological study deals with issues like Neurotransmission, which deal with fields such as Neocortex. His biological study spans a wide range of topics, including Machine learning and Computer vision.

His Independent component analysis research includes themes of Infomax, Blind signal separation, Speech recognition, Algorithm and Brain mapping. His Pattern recognition research is multidisciplinary, incorporating perspectives in Artifact, Voxel, Communication and Signal processing. The concepts of his Electroencephalography study are interwoven with issues in Memory consolidation, Visual perception, Cognition and Scalp.

His most cited work include:

  • An information-maximization approach to blind separation and blind deconvolution (7577 citations)
  • Thalamocortical oscillations in the sleeping and aroused brain (2855 citations)
  • A learning algorithm for boltzmann machines (2745 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Neuroscience, Artificial intelligence, Pattern recognition, Excitatory postsynaptic potential and Neuron. His study in Neuroscience is interdisciplinary in nature, drawing from both Synaptic plasticity and Neurotransmission. Terrence J. Sejnowski has included themes like Machine learning, Computer vision and Electroencephalography in his Artificial intelligence study.

The Excitatory postsynaptic potential study combines topics in areas such as Membrane potential and Postsynaptic potential. His Independent component analysis research includes elements of Infomax, Blind signal separation, Principal component analysis and Speech recognition. His Visual cortex study frequently draws parallels with other fields, such as Receptive field.

He most often published in these fields:

  • Neuroscience (63.29%)
  • Artificial intelligence (33.25%)
  • Pattern recognition (14.89%)

What were the highlights of his more recent work (between 2015-2021)?

  • Neuroscience (63.29%)
  • Artificial intelligence (33.25%)
  • Biophysics (9.26%)

In recent papers he was focusing on the following fields of study:

Terrence J. Sejnowski spends much of his time researching Neuroscience, Artificial intelligence, Biophysics, Electroencephalography and Synaptic plasticity. Neuroscience and Neurotransmission are frequently intertwined in his study. He interconnects Machine learning, Set and Pattern recognition in the investigation of issues within Artificial intelligence.

He works mostly in the field of Biophysics, limiting it down to concerns involving Calcium and, occasionally, Endoplasmic reticulum. His Electroencephalography study integrates concerns from other disciplines, such as Schizophrenia and Memory consolidation. His study looks at the relationship between Synaptic plasticity and topics such as Long-term potentiation, which overlap with Postsynaptic density.

Between 2015 and 2021, his most popular works were:

  • Single-cell Methylomes Identify Neuronal Subtypes and Regulatory Elements in Mammalian Cortex (242 citations)
  • Single-cell Methylomes Identify Neuronal Subtypes and Regulatory Elements in Mammalian Cortex (242 citations)
  • Cortical travelling waves: mechanisms and computational principles. (177 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Neuroscience
  • Statistics

His primary scientific interests are in Neuroscience, Artificial intelligence, Electroencephalography, Biophysics and Calcium. Neuroscience and Synaptic plasticity are commonly linked in his work. His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.

His Pattern recognition research focuses on Electrocorticography and how it relates to Matrix, Partial correlation and Neurophysiology. The study incorporates disciplines such as Visual perception, Schizophrenia and Neuropharmacology in addition to Electroencephalography. His Biophysics study deals with Dendritic spine intersecting with Voltage-dependent calcium channel and Endoplasmic reticulum.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

An information-maximization approach to blind separation and blind deconvolution

Anthony J. Bell;Terrence J. Sejnowski.
Neural Computation (1995)

10613 Citations

A learning algorithm for boltzmann machines

David H. Ackley;Geoffrey E. Hinton;Terrence J. Sejnowski.
Cognitive Science (1985)

4292 Citations

Thalamocortical oscillations in the sleeping and aroused brain

Mircea Steriade;David A. McCormick;Terrence J. Sejnowski.
Science (1993)

3609 Citations

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

Henriette van Praag;Brian R. Christie;Terrence J. Sejnowski;Fred H. Gage.
Proceedings of the National Academy of Sciences of the United States of America (1999)

3284 Citations

The Computational Brain

Patricia Smith Churchland;Terrence J. Sejnowski.
(1992)

2978 Citations

Removing electroencephalographic artifacts by blind source separation.

Tzyy-Ping Jung;Tzyy-Ping Jung;Scott Makeig;Colin Humphries;Te-Won Lee;Te-Won Lee.
Psychophysiology (2000)

2891 Citations

Face recognition by independent component analysis

M.S. Bartlett;J.R. Movellan;T.J. Sejnowski.
IEEE Transactions on Neural Networks (2002)

2709 Citations

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

Anthony J. Bell;Terrence J. Sejnowski.
Vision Research (1997)

2621 Citations

Parallel networks that learn to pronounce English text

Terrence J. Sejnowski;Charles R. Rosenberg.
Complex Systems (1987)

2580 Citations

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

Martin J. Mckeown;Scott Makeig;Greg G. Brown;Tzyy-Ping Jung.
Human Brain Mapping (1998)

2242 Citations

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