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.
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 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.
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.
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.
An information-maximization approach to blind separation and blind deconvolution
Anthony J. Bell;Terrence J. Sejnowski.
Neural Computation (1995)
A learning algorithm for boltzmann machines
David H. Ackley;Geoffrey E. Hinton;Terrence J. Sejnowski.
Cognitive Science (1985)
Thalamocortical oscillations in the sleeping and aroused brain
Mircea Steriade;David A. McCormick;Terrence J. Sejnowski.
Science (1993)
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)
The Computational Brain
Patricia Smith Churchland;Terrence J. Sejnowski.
(1992)
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)
Face recognition by independent component analysis
M.S. Bartlett;J.R. Movellan;T.J. Sejnowski.
IEEE Transactions on Neural Networks (2002)
The "independent components" of natural scenes are edge filters.
Anthony J. Bell;Terrence J. Sejnowski.
Vision Research (1997)
Parallel networks that learn to pronounce English text
Terrence J. Sejnowski;Charles R. Rosenberg.
Complex Systems (1987)
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)
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