CNL Publication Search: Karuvally, A., Sejnowski, T. and Siegelmann, H.T. H. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research

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This database contains all the publications of Terrence J. Sejnowski starting in 1969.

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Found 65 Articles
Found 4 Edited Books
Found 6 Invited Reviews
Found 28 Refereed Conference Proceedings
Found 5 Cited Technical Reports
Found 22 Book Chapters
Found 40 Abstracts
Found 7 Press
Found 1 Dissertation
Found 178 Total.

Articles

Chen, Y. Zhang, H. Cameron, M. Sejnowski, T. Predictive sequence learning in the hippocampal formation, Neuron, 112, 1-14, 2024 (PDF)
Chen, Y. Zhang, H. Sejnowski, T. J. Predictive Sequence Learning in the Hippocampal Formation, BioRxiv, 2022.05.19.492731, 2022 (PDF)
Li, Y., Kim, R., Sejnowski, T.J. Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models, Neural Computation, 33, 3264-3287, 2021 (PDF)
Macpherson T, Churchland A, Sejnowski T. J., DiCarlo J, Kamitani Y, Takahashi H, Hikida T. Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research., Neural Networks, 144, 603-613, 2021 (PDF)
Sejnowski, T. J. The unreasonable effectiveness of deep learning in artificial intelligence, Proceedings of the National Academy of Sciences U.S.A., 48, 30033-30038, 2020 (PDF)
Tsuda, B. Tye, K. M. Siegelmann, H. T. Sejnowski, T. J. A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex, Proceedings of the National Academy of Sciences U.S.A., 117, 29872-29882, 2020 (PDF)
Siddharth, S. Jung, T.-P. Sejnowski, T. J. Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing, IEEE Transactions on Affective Computing, PP(99):1-1, 2019 (PDF)
Ernst, O. K. Bartol. T. M. Sejnowski. T. J. Mjolsness , E Learning moment closure in reaction-diffusion systems with spatial dynamic Boltzmann distributions, Physical Review E, 99, 063315, 2019 (PDF)
Ernst, O. K., Bartol, T., Sejnowski, T. J., Mjolsness. E. Learning dynamic Boltzmann distributions as reduced models of spatial chemical kinetics, The Journal of Chemical Physics, 149, 034107, 2018 (PDF)
Reddy, G. Ng, J. W. Celani, A. Sejnowski, T. J. Vergassola, M. Soaring Like a Bird via Reinforcement Learning in the Field, Nature, 562, 236-239, 2018 (PDF)
Das, A. Sejnowski, T. J. Narrowband and Wideband Off-Grid Direction-of-Arrival (DOA) Estimation via Sparse Bayesian Learning, IEEE Journal of Oceanic Engineering, 43:108-118, 2018 (PDF)
Reddy,G. Celani, A. Sejnowski, T. J. Vergassola, M. Learning to soar in turbulent environments, Proceedings of the National Academy of Sciences of the United States of America, 113 (33), 2016 PMCID:PMC4995969 (PDF)
Saremi, S. Sejnowski, T. J. , The Wilson Machine for Image Modeling, arXiv, 1510.07740v1, 2015 (PDF)
Ritaccio, A.: Brunner, P. Gunduz, A. Hermes, D. Hirsch, L. J. Jacobs, J. Kamada, K. Kastner, S. Knight, R. T. Lesser, R. P. Miller, K. Sejnowski, T. J. Worrell, G. Schalk, G. Proceedings of the Fifth International Workshop on Advances in Electrocorticography, Epilepsy & Behavior, 41, 183-192, 2014 PMCID:PMC4268064 (PDF)
Fields, R. D. Araque, A. Johansen-Berg, H. Lim, S.-S. Lynch, G. Nave, K.-A. Nedergaard, M. Perez, R. Sejnowski, T. J. Wake, H. Glial Biology in Learning and Cognition, The Neuroscientist, 20, 426-431, 2014 (PDF)
Broccard, F. Mullen, T. Chi, Y. M. Peterson, D. Iversen, J. R. Arnold, M. P. Kreutz-Delgado, K. Jung, T.-P. Makeig, S. Poizner, H. Sejnowski, T. J. Cauwenberghs, G. Closed-loop Brain-Machine-Body Interfaces for Noninvasive Rehabilitation of Movement Disorders, Annals of Biomedical Engineering, 8, 1573-1593, 2014 PMCID:PMC4099421 (PDF)
Moldakarimov, S Bazhenov, M Sejnowski, TJ Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning, PLOS Computational Biology, 10 (8): e1003770, 2014 PMCID:4133043 (PDF)
Chukoskie,L. Snider, J. Mozer, M. C. Krauzlis, R. J. Sejnowski, T. J. Learning where to look for a hidden target, Proceedings of the National Academy of Sciences USA, 110, 10438–10445, 2013 PMCID:PMC3690606 (PDF)
Phillips, C. L. Bruno, M. A. Maquet, P. Boly, M. Noirhomme, Q. Schnakers, C. Vanhaudenhuyse, A. Bonjean, M. Hustinx, R. Moonen, G Luxen, A. Laureys, S "Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients, NeuroImage, 56(2):797-808, 2011
Peterson, D.A. Lotz, D.T. Halgren, E. Sejnowski, T. J. Poizner, H Choice Modulates the Neural Dynamics of Prediction Error Processing During Rewarded Learning, NeuroImage, 54: 1385-1394, 2011 PMCID:PMC2997183 (PDF)
Peterson, D. A. Elliott, C. Song, D. D. Makeig, S. Sejnowski, T. J. Poizner, H. Probabilistic Reversal Learning is Impaired in Parkinson's Disease, Neuroscience, 163: 1092-1101, 2009 PMCID:PMC2760640 (PDF)
Conner, J.M. Franks, K.M. Titterness, A.K. Merrill, D.A. Christie, B.R. Sejnowski, T. J. Tuszynski, M.H. NGF Is Essential for Hippocampal Plasticity and Learning, J. Neuroscience, 29: 10833-10899, 2009 PMCID:PMC2765804 (PDF)
Meltzoff, A.N. Kuhl, P.K. Movellan, J. Sejnowski, T. J. Foundations for a New Science of Learning, Science, 325: 284-288, 2009 PMCID:PMC2776823 (PDF)
Goldbaum M.H. Falkenstein I. Kozak I. Hao J. Bartsch D.U. Sejnowski, T. J. Freeman W.R., Analysis with support vector machine shows HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyes., Transactions of the American Ophthalmological Society, 106:196-205, 2008 PMCID:PMC2646437 (PDF)
Finelli, L.A. Haney, S. Bazhenov, M. Stopfer. M. Sejnowski, T. J. Synaptic Learning Rules and Sparse Coding in a Model Sensory System Graham, L. (Ed.), PLoS Comput Biol, 4(4):e1000062. doi:10.1371/journal.pcbi.1000062, 2008 PMCID:PMC2278376 (PDF)
Bowd, C. Medeiros, F. A. Zhang, Z. Zangwill, L. M. Hao, J. Lee, T.-W. Sejnowski, T. J. Weinberg, R. J. Goldbaum, M. H. Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements, Investigative Ophthalmology & Visual Science, Vol. 46, No. 4, 2005 (PDF)
Coggan, J. S. Bartol, T. M. Jr. Esquenazi, E. I. Stiles, J. R. Lamont, S. Martone, M. E. Berg, D. K. Ellisman, M. H. Sejnowski, T. J. Evidence for Ectopic Neurotransmission at a Neuronal Synapse, Science, 39, 446-451, 2005 (PDF)
Bazhenov, M. Stopfer, M. Sejnowski, T. J. Laurent, G. J. Fast Odor Learning Improves Reliability of Odor Responses in the Locust Antennal Lobe, Neuron, 49, 483-492, 2005 (PDF)
Prank, K. Schulze, E. Eckert, O. Nattkemper, T. W. Bettendorf, M. Maser-Gluth, C. Sejnowski, T. J. Grote, A. Penner, E. von zur Muehlen, A. Brabant, G. Machine Learning Approaches for Phenotype-Genotype Mapping: Predicting Heterozygous Mutations in the CYP21B Gene from Steroid Profiles, European Journal of Endocrinology, 153(2), 301-305, 2005 (PDF)
Sample, P. A. Boden, C. Zhang, Z. Pascual, J. Lee, T.-W. Zangwill, L. A. Weinreb, R. N. Crowston, J. G. Hoffmann, E. M. Medeiros, F. A. Sejnowski, T. J. Goldbaum, M. Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields, Investigative Ophthalmology and Visual Science, 46(10):3684-92, 2005 PMCID:PMC1832121 (PDF)
Goldbaum, M. Sample, P. A. Zhang, Z. Chan, K. Hao, J, Lee T.-W. Boden, C. Bourne, R. Zangwill, L. A. Sejnowski, T. J. Spinak, D. Weinreb, R. N. Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects, Investigative Ophthalmology and Visual Science, 46(10):3676-83, 2005 PMCID:PMC1866286 (PDF)
Zangwill, L. M. Chan, K. Bowd, C. Hao, J. Lee, T.-W. Weinberg, R. J. Sejnowski, T. J. Goldbaum, M. H. Heidelberg Retina Tomograph Measurements of the Optic Disc and Parapapillary Retina for Detecting Glaucoma Analyzed by Machine Learning Classifiers, Investigative Ophthalmology & Visual Science, 45(9) 3144-3151, 2004 (PDF)
Casanova, H. Berman, F. Bartol, T. M. Jr. Gokay, E. Sejnowski, T. J. Birnmaum, A. Dongarra, J. Miller, M. Ellisman, M. H. Faerman, M. Obertelli, G. Wolski, R. Pomerantz, S. Stiles, J. R. The Virtual Instrument: Support for Grid-Enabled MCell Simulations, The International Journal of High Performance Computing Applications, 18(1) 3-17, 2004 (PDF)
Sample, P. A. Chan, K. Boden, C. Lee, T.-W. Blumenthal, E. Z. Weinberg, R. J. Bernd, A. Pascual, J. Hao, J. Sejnowski, T. J. Goldbaum, M. H. Using Unsupervised Learning with Variational Bayesian Mixture of Factor Analysis to Identify Patterns of Glaucomatous Visual Field Defects, Investigative Ophthalmology & Visual Science, 45(8) 2596-2605, 2004 (PDF)
Kreutz-Delgado, K. Murray, J. F. Rao, B. D. Engan, K. Lee, T.-W. Sejnowski, T. J. Dictionary Learning Algorithms for Sparse Representation, Neural Computation, 15(2) 349-396, 2003 (PDF)
Chan, K. Lee, T.-W. Sejnowski, T. J. Variational Bayesian Learning of ICA with Missing Data, Neural Computation, 15, 1991–2011, 2003 (PDF)
Sample, P. A. Goldbaum, M. H. Chan, K. Boden, C. Lee, T.-W. Vasile, C. Boehm, A. G. Sejnowski, T. J. Johnson, C. A. Weinberg, R. J. Using Machine Learning Classifiers to Identify Glaucomatous Change Earlier in Standard Visual Fields, Investigative Ophthalmology & Visual Science, 43(8) 2660-2665, 2002 (PDF)
Goldbaum, M. H. Sample, P. A. Chan, K. Williams, J. Lee, T.-W. Blumenthal, E. Z. Girkin, C. A. Zangwill, L. M. Bowd, C. Sejnowski, T. J. Weinberg, R. J. Comparing Machine Learning Classifiers for Diagnosing Glaucoma from Standard Automated Perimetry, Investigative Ophthalmology & Visual Science, Vol. 43, No. 1, 2002 (PDF)
Chan, K. Lee, T.W. Sample, P. A. Goldbaum, M. H. Weinberg, R. J. Sejnowski, T. J. Comparison of Machine Learning and Traditional Classifiers in Glaucoma Diagnosis - Biomedical Engineering, IEEE Transactions on Biomedical Engineering, 49(9) 963-974, 2002 (PDF)
Stewart-Bartlett, M. Movellan, J. R. Sejnowski, T. J. Face Recognition by Independent Component Analysis, IEEE Transactions on Neural Networks, 13(6) 1450-1464, 2002 (PDF)
Wiskott, L. Sejnowski, T. J. Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4) 714-770, 2002 (PDF)
Suri, R. E. Sejnowski, T. J. Spike Propagation Synchronized by Temporally Asymmetric Hebbian Learning, Biological Cybernetics, 87, 440-445, 2002 (PDF)
Chan, K. Lee, T.-W. Sejnowski, T. J. Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components, Journal of Machine Learning Research, 3, 99-114, 2002 (PDF)
Zhang, K. Sejnowski, T. J. Accuracy and Learning in Neuronal Populations, Progress in Brain Research, 130, 333-342, 2001 (PDF)
Coenen, O. J.-M. Arnold, M. P. Sejnowski, T. J. Jabri, M. A. Parallel Fiber Coding in the Cerebellum for Life-Long Learning, Autonomous Robots, 11, 291-297, 2001 (PDF)
Rao, R. P. N. Sejnowski, T. J. Self-Organizing Neural Systems Based on Predictive Learning, Philosophical Transactions of The Royal Society, 361(1807) 1149-1175, 2001 (PDF)
Rao, R. P. N. Sejnowski, T. J. Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning, Neural Computation, 13(10), 2221-2237, 2001 (PDF)
Lewicki, M. S. Sejnowski, T. J. Learning Overcomplete Representations, Neural Computation, 12(2), 337-365, 2000 (PDF)
van Praag, H. Christie, B. R. Sejnowski, T. J. Gage, F. H. Running Enhances Neurogenesis, Learning, and Long-Term Potentiation in Mice, Proceedings of the National Academy of Sciences of the United States of America, 96 (23), 13427-13431, 1999 (PDF)
Stewart-Bartlett, M. Sejnowski, T. J. Learning Viewpoint-Invariant Face Representations from Visual Experience in an Attractor Network, Network: Comput. Neural Syst., 9(3), 399-417, 1998 (PDF)
Quartz, S. R. Sejnowski, T. J. The Neural Basis of Cognitive Development: a Constructivist Manifesto, Behavioral and Brain Sciences, 20(4), 537-596, 1997 (PDF)
Montague, P. R. Dayan, P. Sejnowski, T. J. A Framework for Mesencephalic Dopamine Systems Based on Predictive Hebbian Learning, Journal of Neuroscience, 16(5), 1936-1947, 1996 (PDF)
Dayan, P. Sejnowski, T. J. Exploration Bonuses and Dual Control, Machine Learning, 25(1), 1996 (PDF)
Bell, A. J. Sejnowski, T. J. Learning the Higher-Order Structure of a Natural Sound, Network: Computation in Neural Systems, 7, 261-266, 1996 (PDF)
du Lac, S., Raymond, J.L., Sejnowski, T.J., Lisberger, S.G. Learning and memory in the vestibulo-ocular reflex, Annu Rev Neurosci., 18, 409-41, 1995 (PDF)
Montague, P. R. Dayan, P. Person, C. Sejnowski, T. J. Bee Foraging in Uncertain Environments Using Predictive Hebbian Learning, Nature, 377, 725-728, 1995 (PDF)
Dayan, P. Sejnowski, T. J. TD(lambda) Converges With Probability 1, Machine Learning, 14, 295-301, 1994 (PDF)
Montague, P. R. Sejnowski, T. J. The Predictive Brain: Temporal Coincidence and Temporal Order in Synaptic Learning Mechanisms, Learning & Memory, 1, 1-33, 1994 (PDF)
Lockery, S. R. Sejnowski, T. J. A Lower Bound on the Detectability of Nonassociative Learning in the Local Bending Reflex of the Medicinal Leech, Behavioral and Neural Biology, 59, 208-224, 1993 (PDF)
Dayan, P. Sejnowski, T. J. The Variance of Covariance Rules for Associative Matrix Memories and Reinforcement Learning, Neural Computation, 5, 205-209, 1993 (PDF)
Lisberger, S. G. Sejnowski, T. J. Motor Learning in a Recurrent Network Model Based on the Vestibulo-Ocular Reflex, Nature, 360, 159-161, 1992 (PDF)
Fang, Y. Sejnowski, T. J. Faster Learning for Dynamic Recurrent Backpropagation, Neural Computation, 2,270-273, 1990 (PDF)
Sejnowski, T. J. Kienker, P. K. Hinton, G. E. Learning Symmetry Groups with Hidden Units: Beyond the Perceptron, Physica, 22D, 260-275, 1986 (PDF)
Ackley, D. H. Hinton, G. E. Sejnowski, T. J. A learning algorithm for Boltzmann machines, Cognitive Science, 9, 147-169, 1985 (PDF)
Ackley, D. H. Hinton, G. E. Sejnowski, T. J. A Learning Algorithm for Boltzmann Machines*, Cognitive Science, 9, 147-169, 1985 (PDF)

Edited Books

Lee, T.-W. Jung, T.-P. Makeig, S. Sejnowski, T. J. Third International Conference on Independent Component Analysis and Blind Signal Separation, San Diego, CA, 2001
Hinton, G. E. Sejnowski, T. J. Unsupervised Learning: Foundations of Neural Computation, MIT Press, Cambridge, MA, MIT Press Publishers, 1999 (PDF)
Connectionist Models, Proceedings of the 1990 Summer School Touretzky, D. S. Elman, J. L. Sejnowski, T. J. Hinton, G. E. (Ed.), San Mateo, CA: Morgan Kaufmann Publishers, 1991 (PDF)
Proceedings of the 1988 Connectionist Models Summer School Touretzky, D. S. Hinton, G. E. Sejnowski, T. J. (Ed.), San Mateo, CA: Morgan Kaufmann Publishers, 1989 (PDF)

Invited Reviews

Hayes TL, Krishnan, G.P., Bazhenov, M., Siegelmann, H.T., Sejnowski, T.J., Kanan, C. Replay in Deep Learning: Current Approaches and Missing Biological Elements, Neural Computation, 33, 2908-2950, 2021 (PDF)
Koroshetz, W. Sejnowski, T. J., , Nature Neuroscience 19, 1120 2016. Brain Research through Advancing Innovative Neurotechnologies, Nature Neuroscience, 19, 1120-1121, 2016 (PDF)
Sejnowski, T. J. Learning optimal strategies in complex environments, Proceedings of the National Academy of Sciences of the United States of America, 107: 20151–20152, doi/10.1073/pnas.1014954107, 2010 (PDF)
Finelli, L. A. Sejnowski, T. J. What Is Consolidated During Sleep-Dependent Motor Skill Learning?, Behavioral and Brain Sciences, 28, 70-71, 2005 (PDF)
Sejnowski, T. J. Computing with Connections: Review of "The Connection Machine" by W. Daniel Hillis, Journal of Mathematical Psychology, 31, 203-210, 1987 (PDF)
Sejnowski, T. J. Computing with connections: Review of "The Connection Machine" by W. Daniel Hillis, Journal of Mathematical Psychology, 31, 203-210, 1987

Refereed Conference Proceedings

Keller, T. A., Muller, L., Sejnowski, T., Welling, M. Traveling Waves Encode the Recent Past and Enhance Sequence Learning, International Conference on Learning Representations (ICLR), 2024 (PDF)
Karuvally, A., Sejnowski, T. and Siegelmann, H.T. H. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research Hidden Traveling Waves Bind Working Memory Variables in Recurrent Neural Networks, Proceedings of the 41st International Conference on Machine Learning, In Proceedings of Machine Learning Research, 235:23266-23290, 2024 (PDF)
Balls, G.T. Baden, S.B. Kispersky, T. Bartol, T.M. Sejnowski, T. J. A large scale monte carlo simulator for cellular microphysiology - Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International, Proceedings of the 18th International Parallel and Distributed Processing Symposium, (IPDPS-04), 2004 (PDF)
Chan, K Lee, T-W. Sejnowski, T. J. Handling Missing Data with Variational Bayesian Learning of ICA, Advances in Neural Information Processing Systems, 15, 905-912, 2002 (PDF)
Rao, R. P. N. Sejnowski, T. J. Predictive Sequence Learning in Recurrent Neocortical Circuits, Advances in Neural Information Processing Systems, 12, 164-170, 2000 (PDF)
Rao, R. P. N. Sejnowski, T. J. Direction Selectivity from Predictive Sequence Learning in Recurrent Neocortical Circuits, Proceedings of the 6th Joint Symposium on Neural Computation, California Institute of Technology, Pasadena, CA, 119-126, 1999 (PDF)
Kreutz-Delgado, K. Rao, B. D. Engan, K. Lee, T.-W. Sejnowski, T. J. Learning Overcomplete Dictionaries: Convex/Schur-Convex (SCS) Log-Priors, Factorial Codes, and Independent/ Dependent Component Analysis (I/DCA), Proceedings of the 6th Joint Symposium on Neural Computation, California Institute of Technology, Pasadena, CA, 72-78, 1999
Lewicki, M. S. Sejnowski, T. J. Learning Nonlinear Overcomplete Representations for Efficient Coding Kearns, M. Jordan, M. I. Solla, S. (Ed.), Advances in Neural Information Processing Systems, 10, 556-562, 1998 (PDF)
Lewicki, M. S. Sejnowski, T. J. Bayesian Unsupervised Learning of Higher Order Structure Mozer, M. Jordan, M. I. Petsche, T. (Ed.), Advances in Neural Information Processing Systems, 9, MIT Press, Cambridge, MA. 529-535, 1997 (PDF)
Stensmo, S. Sejnowski, T. J. Learning Decision Theoretic Utilities Through Reinforcement Learning Mozer, M. Jordan, M. I. Petsche, T. (Ed.), Advances in Neural Information Processing Systems, 9, MIT Press, Cambridge, MA., 1061-1067, 1997 (PDF)
Eisele, M. Sejnowski, T. J. Model-Based Reinforcement Learning by Pyramidal Neurons: Robustness of the Learning Rule, Proceedings of the 4th Joint Symposium on Neural Computation, University of California, San Diego and University of Southern California 6, Institute for Neural Computation, La Jolla, CA, 83-90, 1997 (PDF)
Stensmo, M. Sejnowski, T. J. Automated Medical Diagnosis Based on Decision Theory and Learning from Cases, World Congress on Neural Networks, San Diego, CA, 1227-1231, 1996 (PDF)
Coenen, O. J.-M.D. Sejnowski, T. J. Learning to Make Predictions in the Cerebellum May Explain the Anticipatory Modulation of the Vestibulo-Ocular Reflex (VOR) Gain with Vergence, Proceedings of the 3rd Joint Symposium on Neural Computation, University of California, San Diego and California Institute of Technology, Institute for Neural Computation, La Jolla, CA, 202-221, 1996 (PDF)
Stensmo, M. Sejnowski, T. J. A Mixture Model System for Medical and Machine Diagnosis Tesauro, G. Touretzky, D. S. Leen, T. (Ed.), Advances in Neural Information Processing Systems, 7, MIT Press, Cambridge, MA 1077-1084, 1995 (PDF)
Doya, K. Sejnowski, T. J. A Novel Reinforcement Model of Birdsong Vocalization Learning Tesauro, G. Touretzky, D. S. Leen, T. (Ed.), Advances in Neural Information Processing Systems, 7, MIT Press, Cambridge, MA 101-108, 1995 (PDF)
Schraudolph, N. N. Sejnowski, T. J. Plasticity-Mediated Competitive Learning Tesauro, G. Touretzky D. Leen, T. (Ed.), Advances in Neural Information Processing Systems, 7, MIT Press, Cambridge, MA, 475-480, 1995 (PDF)
Sejnowski, T. J. Dayan, P. Montague, P. R. Predictive Hebbian Learning, 8th ACM Conference on Computational Learning Theory (COLT), Santa Cruz, 15-18, 1995 (PDF)
Pouget, A. Deffayet, C. Sejnowski, T. J. Reinforcement Learning Predicts the Site of Plasticity for Auditory Remapping in the Barn Owl Tesauro, G. Touretzky, D. S. Leen, T. (Ed.), Advances in Neural Information Processing Systems, 7, MIT Press, Cambridge, MA, 125-132, 1995 (PDF)
Stensmo, M. Sejnowski, T. J. Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis, Proceedings of the 2nd Joint Symposium on Neural Computation, University of California, San Diego and California Institute of Technology, Institute for Neural Computation, La Jolla, CA, 9-16, 1995 (PDF)
Berns, G. S. Sejnowski, T. J. A Model of Basal Ganglia Function Unifying Reinforcement Learning and Action Selection, Proceedings of the Joint Symposium on Neural Computation, University of California, San Diego and California Institute of Technology, Institute for Neural Computation, La Jolla, CA, 129-148, 1994 (PDF)
Montague, P. R. Dayan, P. Sejnowski, T. J. Foraging in an Uncertain Environment Using Predictive Hebbian Learning, Advances in Neural Information Processing Systems, 6, Morgan Kaufman Publishers, San Mateo, CA, 598-605, 1994 (PDF)
Schraudolph, N. N. Dayan, P. Sejnowski, T. J. Temporal Difference Learning of Position Evaluation in the Game of Go, Advances in Neural Information Processing Systems, 6, Morgan Kaufman Publishers, San Mateo, CA, 817-824, 1994 (PDF)
Coenen, O. J.-M.D. Sejnowski, T. J. Lisberger, S. G. Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex Giles, C. L. Hanson, S. J. Cowan, J. D. (Ed.), Advances in Neural Information Processing Systems, 5, San Mateo, CA: Morgan Kaufman Publishers, 961-96, 1993 (PDF)
Schraudolph, N. N. Sejnowski, T. J. Competitive Anti-Hebbian Learning of Invariants Moody, J. E. Hanson, S. J. Lippmann, R. P. (Ed.), Advances in Neural Information Processing Systems, 4, San Mateo, CA: Morgan Kaufmann Publishers, 1017-1024, 1992 (PDF)
Lockery, S. R. Fang, Y. Sejnowski, T. J. A dynamical neural network model of sensorimotor transformations in the leech - Neural Networks, 1990., 1990 IJCNN International Joint Conference on, IJCNN International Joint Conference on Neural Networks, (Cat. No. 90CH2879-5). New York, NY, USA: IEEE, 183-188 vol. 1, 1990 (PDF)
Qian, N. Sejnowski, T. J. Learning to Solve Random-Dot Stereograms of Dense and Transparent Surfaces with Recurrent Backpropagation Touretzky, D. S. Hinton, G. E. Sejnowski, T. J. (Ed.), Proceedings of the 1988 Connectionist Models Summer School, San Mateo, CA: Morgan Kaufmann Publishers, 1989 (PDF)
Hinton, G. E. Sejnowski, T. J. Learning in Boltzmann Machines, Cognitiva, 85, Paris, France, 1985 (PDF)
Hinton, G. E. Sejnowski, T. J. Learning Semantic Features, Proceedings 6th Annual Conference of the Cognitive Science Society, Boulder, 1984 (PDF)

Cited Technical Reports

Chen, Y., Zhang, H., Sejnowski, TJ., , 2022.05.19.492731 (2022). Predictive Sequence Learning in the Hippocampal Formation, BioRxiv, 2022.05.19.492731, 2023
Keller, T. A., Muller, L., Sejnowski, T., & Welling, M. Traveling Waves Encode the Recent Past and Enhance Sequence Learning, arXiv, 2309.08045, 2023 (PDF)
Ernst, O.K., Bartol, T., Sejnowski, T. J., Mjolsness, E. Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics, arXiv:2109.05053, 2021
Siddharth, S. Patel, A. Jung, T.-P. Sejnowski, T. J. An Affordable Bio-Sensing and Activity Tagging Platform for HCI Research, arXiv:1802.07852, 2018 (PDF)
Lisberger, S. G. Sejnowski, T. J. Computational Analysis Suggests a New Hypothesis for Motor Learning in the Vestibulo-Ocular Reflex, Institute for Neural Computation Technical Report Series, INC-9201, 1992 (PDF)

Book Chapters

Moldakarimov, S. Sejnowski, T. J. Neural Computation Theories of Learning Byrne, J. H. (Ed.), In: Learning and Memory: A Comprehensive Reference, 2nd Ed., 579-589, Elsevier, 2017 (PDF)
Chukoskie,L. Snider, J. Mozer, M. C. Krauzlis, R. J. Sejnowski, T. J. Learning where to look for a hidden target, In: In the Light of Evolution VII: The Human Mental Machinery, Washington DC: National Academies Press, 2014 (PDF)
Moldakarimov, S. Sejnowski, T. J. Neural Computation Theories of Learning J. Byrne (Ed.), In: Learning and Memory: A Comprehensive Reference, Vol. 4, pp 667-6809, Oxford, Elsevier, 2008 (PDF)
Bell, A. J. Sejnowski, T. J. Learning to Find Independent Components in Natural Scenes Fahle, M. Poggio, T. (Ed.), In: Perceptual Learning, MIT Press, Cambridge, MA, 355-36, 2002 (PDF)
Hinton, G. E. Sejnowski, T. J. Learning and Relearning in Boltzmann Machines Jordan, M. Sejnowski, T. J. (Ed.), In: Graphical Models: Foundations of Neural Computation, MIT Press, Cambridge, MA, 45-76, 2001
Schraudolph, N. N. Dayan, P. Sejnowski, T. J. Learning to Evaluate Go Positions Via Temporal Difference Methods Baba, N. Jain, L. C. (Ed.), In: Computational Intelligence in Games, Springer Verlag, Berlin 62 (4) 77-98, 2001 (PDF)
Rao, R. P. N. Sejnowski, T. J. Predictive Learning of Temporal Sequences in Recurrent Neocortical Circuits Bock, G. Goode, J. (Ed.), In: Complexity in Biological Information Processing, John Wiley and Sons LTD, England, 208-233, 2001 (PDF)
Doya, K. Sejnowski, T. J. A Computational Model of Avian Song Learning Gazzaniga, M. S. (Ed.), In: The New Cognitive Neurosciences, 2nd edition, 469-482, MIT Press, Cambridge, MA, 2000 (PDF)
Montague, P. R. Dayan, P. Sejnowski, T. J. Volume Learning: Signaling Covariance Through Neural Tissue Bower, J. M. Eeckman, F. H. (Ed.), In: Computation and Neural Systems, Norwell, MA.: Kluwer Academic Publishers, 377-382, 2000 (PDF)
Hinton, G. E. Sejnowski, T. J. Unsupervised Learning: Foundations of Neural Computation Hinton, G. E. Sejnowski, T. J. (Ed.), In: Unsupervised Learning: Foundations of Neural Computation, MIT Press, Cambridge, MA, vii-xvi, 1999 (PDF)
Doya, K. Sejnowski, T. J. A Computational Model of Birdsong Learning by Auditory Experience and Auditory Feedback Poon, P. Brugge, J. (Ed.), In: Auditory Processing and Neural Modeling, Plenum Press, 77-88, 1998 (PDF)
Stewart-Bartlett, M. Sejnowski, T. J. Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association Wechsler, H. Phillips, P. J. Bruce, V. Fogelman-Soulie, S. Huang, T. (Ed.), In: Face Recognition: From Theory to Applications, NATO ASI Series F. Springer-Verlag, 381-390, 1998 (PDF)
Bartlett, M. S. Sejnowski, T. J. Unsupervised Learning of Invariant Representations of Faces Through Temporal Association Bower, J. M. (Ed.), In: Computational Neuroscience: Trends in Research 1995, San Diego, CA: Academic Press, 317-322, 1996 (PDF)
Doya, K. Sejnowski, T. J. Burrows, M. Matheson, T. Newland, P. L. Schuppe, H. A Model of Birdsong Vocalization Learning, In: Nervous Systems and Behavior, Georg Thieme Verlag, Stuttgart, Germany, 76, 1995
Sejnowski, T. J. Neural Computation: Approaches to Learning Squire, L. (Ed.), In: Encyclopedia of Learning and Memory, New York: MacMillan Publishing Company, 1993 (PDF)
Sejnowski, T. J. Tesauro, G. Building Network Learning Algorithms from Hebbian Synapses McGaugh, J. L. Weinberger, N. M. Lynch, G. (Ed.), In: Brain Organization and Memory: Cells, Systems and Circuits, New York: Oxford University Press, 338-355, 1989 (PDF)
Sejnowski, T. J. Rosenberg, C. R. Learning and Representation in Connectionist Models Gazzaniga, M. S. (Ed.), In: Perspectives in Memory Research, Cambridge: MIT Press, 135-178, 1988 (PDF)
Qian, N. Sejnowski, T. J. Learning to Predict the Secondary Structure of Globular Proteins Lee, Y. C. (Ed.), In: Evolution, Learning and Cognition, Singapore: World Scientific, 257-276, 1988
Sejnowski, T. J. Neural Network Learning Algorithms Eckmiller, R. Malsburg, C. von (Ed.), In: Neural Computers, Berlin: Springer-Verlag, 291-300, 1988 (PDF)
Sejnowski, T. J. Hinton, G. E. Separating Figure from Ground with a Boltzmann Machine Arbib, M. A. Hanson, A. R. (Ed.), In: Vision, Brain and Cooperative Computation, Cambridge: MIT Press, 703-724, 1987 (PDF)
Hinton, G. E. Sejnowski, T. J. Learning and Relearning in Boltzmann Machines McClelland, J. Rumelhart, D. (Ed.), In: Explorations in the Microstructure of Cognition 1: Foundations, Cambridge: MIT Press, 282-317, 1986 (PDF)
Hinton, G. E. Sejnowski, T. J. Learning in Boltzmann Machines / Apprentissage Dans Les Machines De Boltzmann, In: Cognitiva, 85, Paris, 284-290, 1985 (PDF)

Abstracts

Knickrehm, J. Kim, R. Lainscsek, C. Sampson, A. L. Gur, R. C. Gur, R. E. The Cogs Investigators, Sejnowski, T. J. LIGHT, G. A. Machine learning approaches to characterize neurophysiological and cognitive measures predictive of schizophrenia diagnosis, Society for Neuroscience Abstracts, 2019
Broccard, F. D. Mullen, T. Chi, Y. Peterson, D. Iversen, J. R. Arnold, M. P. Kreutz-Delgado, K. Jung, T.-P. Makeig, S. Poizner, H. Sejnowski, T. J. Cauwenberghs, G. Closed-loop brain-machine-body interface for noninvasive rehabilitation of movement disorders, Society for Neuroscience Abstracts, 2014 (PDF)
Moldakarimov, S. Bazhenov, M. Sejnowski, T. J. Modulatory feedback inputs into V1 mediate visual perceptual learning, Society for Neuroscience, 2012 (PDF)
Moldakarimov, S., Bazhenov, M., Sejnowski, T. J. Plasticity of top-down feedback projections to primary visual cortex may underlie perceptual learning, Society for Neuroscience Abstracts, 2011
Broccard, F. D., Sejnowski, T. J., Cauwenberghs, G. Temporal dynamics of human and machine reinforcement learning in the game of go, Society for Neuroscience Abstracts, 2011
Peterson, D.A. Mullane, M. Saproo, S. Tran, C. Yazdani, M. Lee, D. Sejnowski, T. J. Poizner, H. Tougher decisions make rougher moves: The kinematics of reaching to make choices during rewarded learning, 2010 (PDF)
Moldakarimov, S. Bazhenov, M. Sejnowski, T. J. Feedback model of visual perceptual learning, 2010 (PDF)
Peterson, D. A. Lotz, D. T. Ahn, A. Halgren, E. Makeig, S. Sejnowski, T. J. Poizner, H. Frontocentral EEG dissociates learning- and decision making-based expected value, 2009 (PDF)
Peterson, D. A. Lotz, D. T. Elliott, C. Makeig, S. Sejnowski, T. J. Poizner, H. Alpha Desynchronization Reflects Prediction Error in Rewarded Learning, Society of Neuroscience Abstracts, 2008 (PDF)
Chukoskie, L. Albright, T.D. Song, D.D. Sejnowski, T. J. Poizner, H. Implicit Learning of Eye Movement Search in Parkinson's Disease, Society of Neuroscience Abstracts, 2008 (PDF)
Peterson, D. A. Elliott, C. Song, D. D. Makeig, S. Sejnowski, T. J. Poizner, H. Dopamine Deficiency in Parkinsonâs disease compromises adaptations in rewarded learning, Movement Disorders Supplement, 23 (1) S105-S105, 2008 (PDF)
Cortes, J.M. Sejnowski, T. J. Van Rossum, M. Weight-Dependent Learning Rules with Long Temporal Correlations, Society for Neuroscience Abstracts, 2007 (PDF)
Pascual, J. P. Zhang, Z. Hughes, A. J. Hao, J. Lee, T.–W. Sejnowski, T. J. Goldbaum, M. H. Weinreb, R. N. Sample, P.A. Diagnosing Glaucoma from Frequency Doubling Technology Perimetry Using Supervised Machine Learning Classifiers, Invest. Ophthalmol. Vis. Sci., 45: 2124, 2004 (PDF)
Goldbaum, M. H. Sample, P. A. Zangwill, L. M. Bowd, C. Boden, C. Lee, T.–W. Zhang, Z. Hao, J. Sejnowski, T. J. Weinreb, R. N. Probability of Glaucoma Determined from Standard Automated Perimetry and from Optic Disk Topography using Relevance Vector Machine Classifiers, Invest. Ophthalmol. Vis. Sci., 45: 2137, 2004 (PDF)
Bowd, C. Medeiros, F. A. Zangwill, L. M. Zhang, Z. Hao, J. Chan, K. Lee, T.–W. Goldbaum, M. H. Sejnowski, T. J. Weinreb, R. N. Support Vector Machine Analysis of VCC Scanning Laser Polarimetry RNFL Thickness Measurements, Invest. Ophthalmol. Vis. Sci., 45: 3405, 2004 (PDF)
Sample, P. A. Zhang, Z. Pascual, J. Chan, K. Boden, C. Hao, J. Lee, T.–W. Weinreb, R. N. Sejnowski, T. J. Goldbaum, M Unsupervised Machine Learning with Independent Component Analysis Identifies Areas of Progression in Glaucomatous Visual Fields, Investigative Ophthalmology and Visual Science, 45: 3471, 2004 (PDF)
Boden, C. Chan, K. Goldbaum, M. Lee, T. Sejnowski, T. J. Hao, J. Vasile, C. Medeiros, F. A. Weinreb, R. N. Sample, P. A. Assessing Validity of Visual Field Clustering Schemes for Standard Perimetry Using Machine Learning Classifiers, Invest. Ophthalmol. Vis. Sci., 44: 60, 2003 (PDF)
Zangwill, L. M. Chan, K. Bowd, C. Medeiros, F. Goldbaum, M. H. Lee, T. Sejnowski, T. J. Weinreb, R. N. Comparing Confocal Scanning Laser Ophthalmoscopy Measurements of the Optic Nerve Head and Peripapillary Retina for Detecting Glaucoma using Machine Learning Classifiers, Invest. Ophthalmol. Vis. Sci., 44: 981, 2003 (PDF)
Goldbaum, M. H. Sample, P.A. Chan, K. Lee, T. McGuire, D. Sejnowski, T. J. Weinreb, R. N. Shortened Perimetry for Glaucoma With Top 10 Locations Derived by Feature Selection With Machine Learning Classifiers, Invest. Ophthalmol. Vis. Sci., 44: 1041, 2003 (PDF)
Finelli, L. A. Haney, S. Bazhenov, M. Stopfer, M. Sejnowski, T. J. Laurent, G. J. Effects of a Synaptic Learning Rule on the Sparseness of Odor Representations in a Model of the Locust Olfactory System, Society for Neuroscience Abstracts, 29, 2003 (PDF)
Goldbaum, M. H. Sample, P. A. Chan, K. Lee, T.-W. McGuire, D. Sejnowski, T. J. Weinreb, R. N. Analysis of Glaucomatous Visual Field Patterns Found with Unsupervised Learning Using Independent Component Analysis and Principal Component Analysis, The Association for Research in Vision and Ophthalmology Abstracts, 43:S2178, 2002 (PDF)
Laurent, G. J. Bazhenov, M. Stopfer, M. Sejnowski, T. J. Fast Odor Learning and Reliability of Odor Responses in the Locust Antennal Lobe, Society for Neuroscience Abstracts, 28, 2002 (PDF)
Boden, C. Chan, K. Goldbaum, M. Lee, T.-W. Sejnowski, T. J. Boehm, A. G. Aihara, M. Weinreb, R. N. Sample, P. A. Machine Learning Classifiers in the Diagnosis and Follow-Up of Glaucoma Using Short-Wavelength Automated Perimetry (SWAP), The Association for Research in Vision and Ophthalmology Abstracts, 43:S2127, 2002 (PDF)
Sample, P. A. Goldbaum, M. H. Chan, K. Boden, C. Lee, T.-W. Boehm, A. Vasile, C. Sejnowski, T. J. Johnson, C. A. Weinreb,R. N Predicting Development of Abnormal Standard Visual Fields in Ocular Hypertensive Eyes: Machine Learning Classifiers and Statpac-Like Analysis, The Association for Research in Vision and Ophthalmology Abstracts, 43:S1939, 2002 (PDF)
Sample, P. A. Goldbaum, M. H. Chan, K.-L. Boden, C. Lee, T.-W. Sejnowski, T. J. Johnson, C. A. Weinreb, R. N. Machine Classifiers Predict Development of Abnormal Standard Visual Fields in Ocular Hypertensive Eyes, North American Permetric Society Meeting, Skaneateles, New York, 2001 (PDF)
van Praag, H. Christie, B. R. Sejnowski, T. J. Gage, F. H. Running Enhances Neurogenesis, Learning and Long-Term Potentiation (LTP) in Mice, Society for Neuroscience Abstracts, 25, 888, 1999 (PDF)
Eisele, M. Sejnowski, T. J. Unsupervised and Reinforcement Learning by Pyramidal Neurons in a Layered Model of Neocortex, Society for Neuroscience Abstracts, 24, 1571, 1998 (PDF)
Coenen, O. J.-M.D. Sejnowski, T. J. A Model of Adaptation in the Cerebellum for Learning the Modulation of the Vestibulo-Ocular Reflex (VOR), Society for Neuroscience Abstracts, 22, 1093, 1996 (PDF)
Stewart-Bartlett, M. Sejnowski, T. J. Learning Viewpoint Invariant Representations of Faces in an Attractor Network, Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, University of California, San Diego, La Jolla, CA, 730, 1996 (PDF)
Doya, K. Sejnowski, T. J. A Computational Model of Birdsong Vocalization Learning, Fourth IBRO World Congress of Neuroscience Abstracts, 502, 1995
Berns, G. S. Sejnowski, T. J. A Computational Model of Basal Ganglia Function with Output Selection and Reinforcement Learning, Society for Neuroscience Abstracts, 20, 2, 1994 (PDF)
Zemel, R. S. Nowlan, S. J. Sejnowski, T. J. A Computational Model of Motion Processing in Area Mst: Learning to Segment Three-Dimensional Moving Objects, Society for Neuroscience Abstracts, 20, 772, 1994 (PDF)
Doya, K. Sejnowski, T. J. A Computational Model of Song Learning in the Anterior Forebrain Pathway of the Birdsong Control System, Society for Neuroscience Abstracts, 20, 166, 1994 (PDF)
Pouget, A Montague, P. R. Dayan, P. Sejnowski, T. J. A Developmental Model of Map Registration in the Superior Colliculus Using Predictive Hebbian Learning, Society for Neuroscience Abstracts, 19, 858, 1993 (PDF)
Quartz, S. R. Dayan, P. Montague, P. R. Sejnowski, T. J. Expectation Learning in the Brain Using Diffuse Ascending Projections, Society for Neuroscience Abstracts, 18, 1210, 1992 (PDF)
Sejnowski, T. J. Lisberger, S. G. Sites of Motor Learning in the Vesibulo-Ocular Reflex (VOR) Predicted by a Dynamical Network Model, Society for Neuroscience Abstracts, 17, 1382, 1991 (PDF)
Lockery, S. R. Sejnowski, T. J. Models of Learning Without Detectable Synaptic Plasticity in the Leech, Society for Neuroscience Abstracts, 16, 626, 1990 (PDF)
Sejnowski, T. J. Language Learning in Massively-Parallel Networks, Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics, June 10-13, 1986
Cohen, N. J. Abrams, I. Harley, W. S. Tabor, L. Gordon, B. Sejnowski, T. J. Perceptual Skill Learning and Repetition Priming for Novel Materials in Patients, Normal Subjects, and Neuron-Like Network Models, Society for Neuroscience Abstracts, 12, 1162, 1986 (PDF)
Cohen, N. J. Abrams, I. Harley, W. S. Tabor, L. Sejnowski, T. J. Skill Learning and Repetition Priming in Symmetry Detection: Parallel Studies of Human Subjects and Connectionist Models, 8th Annual Conference of the Cognitive Science Society, Amherst, MA, 1986 (PDF)

Press

Nair, P. Sejnowski, T. J. QnAs with Terrence J. Sejnowski, Proceedings of the National Academy of Sciences of the United States of America, 107, 20601, 2010 (PDF)
Walter, N. Wetli, M. Ein bisschen Wissen ist vermutlich gut (A Little Knowledge Is Probably Good), SonntagsZeitung, 22. Jahrgang/Nr. 12 - 23. März 2008, 2008 (PDF)
Hopkin, Karen Physics Meets the Brain, The Scientist, Vol 20, Issue 12, p 54, 2006 (PDF)
LaFee, Scott The Stuff of Memories, San Diego Union Tribune, November 30, 2006, 2006 (PDF)
Rosenthal, Jack Mnemonics, New York Times Magazine, 2005 (PDF)
Ehrenfeld, Temma What's in Your Face, Newsweek, June 9, 2003 issue, 2003 (PDF)
Bains, Sunny Backgammon, Anyone? Neural Learning Theory Tested, EE Times, Issue: 1026, 1998 (PDF)

Dissertation

Stewart-Bartlett, M. Face Image Analysis by Unsupervised Learning and Redundancy Reduction, 1998