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ORCIDGarrett T. Kenyon
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- affiliation: Los Alamos National Laboratory, Biophysics Group, USA
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Journal Articles
- 2023
[j17]Sundar Kunwar
, Zachary Jernigan, Zach Hughes, Chase Somodi, Michael Saccone, Francesco Caravelli, Pinku Roy, Di Zhang, Haiyan Wang, Quanxi Jia, Judith L. MacManus-Driscoll, Garrett T. Kenyon, Andrew Sornborger, Wanyi Nie, Aiping Chen:
An Interface-Type Memristive Device for Artificial Synapse and Neuromorphic Computing. Adv. Intell. Syst. 5(8) (2023)- [j16]Sundar Kunwar, Zachary Jernigan, Zach Hughes, Chase Somodi, Michael D. Saccone, Francesco Caravelli, Pinku Roy, Di Zhang, Haiyan Wang, Quanxi Jia, Judith L. MacManus-Driscoll, Garrett T. Kenyon, Andrew Sornborger, Wanyi Nie, Aiping Chen:
An Interface-Type Memristive Device for Artificial Synapse and Neuromorphic Computing. Adv. Intell. Syst. 5(8) (2023) - 2022
- [j15]James B. Aimone, Prasanna Date, Gabriel A. Fonseca Guerra, Kathleen E. Hamilton, Kyle Henke, Bill Kay, Garrett T. Kenyon, Shruti R. Kulkarni, Susan M. Mniszewski, Maryam Parsa, Sumedh R. Risbud, Catherine D. Schuman, William Severa, J. Darby Smith:
A review of non-cognitive applications for neuromorphic computing. Neuromorph. Comput. Eng. 2(2): 32003 (2022) - [j14]Kyle Henke, Garrett T. Kenyon, Ben Migliori:
Fast Post-Hoc Normalization for Brain Inspired Sparse Coding on a Neuromorphic Device. IEEE Trans. Parallel Distributed Syst. 33(2): 302-309 (2022) - 2018
- [j13]Will Fischer, Sanketh S. Moudgalya, Judith D. Cohn, Nga T. T. Nguyen, Garrett T. Kenyon:
Sparse coding of pathology slides compared to transfer learning with deep neural networks. BMC Bioinform. 19-S(18): 9-17 (2018) - 2011
- [j12]Vadas Gintautas, Michael I. Ham, Benjamin Kunsberg, Shawn Barr, Steven P. Brumby, Craig Rasmussen, John S. George, Ilya Nemenman, Luís M. A. Bettencourt, Garrett T. Kenyon:
Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception. PLoS Comput. Biol. 7(10) (2011) - 2008
- [j11]Dylan G. Allegretti, Garrett T. Kenyon, William C. Priedhorsky:
Cellular Automata for Distributed Sensor Networks. Int. J. High Perform. Comput. Appl. 22(2): 167-176 (2008) - 2007
- [j10]Reid B. Porter, Jan R. Frigo, Al Conti, Neal R. Harvey, Garrett T. Kenyon, Maya B. Gokhale:
A reconfigurable computing framework for multi-scale cellular image processing. Microprocess. Microsystems 31(8): 546-563 (2007) - [j9]Jeremy A. Miller, Garrett T. Kenyon:
Extracting Number-Selective Responses from Coherent Oscillations in a Computer Model. Neural Comput. 19(7): 1766-1797 (2007) - 2006
- [j8]Greg J. Stephens, Sergio Neuenschwander, John S. George, Wolf Singer, Garrett T. Kenyon:
See globally, spike locally: oscillations in a retinal model encode large visual features. Biol. Cybern. 95(4): 327-348 (2006) - 2005
- [j7]John M. Galbraith, Garrett T. Kenyon, Richard W. Ziolkowski:
Time-to-Collision Estimation from Motion Based on Primate Visual Processing. IEEE Trans. Pattern Anal. Mach. Intell. 27(8): 1279-1291 (2005) - 2004
- [j6]Garrett T. Kenyon, James Theiler, John S. George, Bryan J. Travis, David W. Marshak:
Correlated Firing Improves Stimulus Discrimination in a Retinal Model. Neural Comput. 16(11): 2261-2291 (2004) - [j5]Garrett T. Kenyon, Dan Hill, James Theiler, John S. George, David W. Marshak:
A theory of the Benham Top based on center-surround interactions in the parvocellular pathway. Neural Networks 17(5-6): 773-786 (2004) - [j4]Garrett T. Kenyon, Bryan J. Travis, James Theiler, John S. George, Greg J. Stephens, David W. Marshak:
Stimulus-specific oscillations in a retinal model. IEEE Trans. Neural Networks 15(5): 1083-1091 (2004) - 2003
- [j3]Garrett T. Kenyon, Bryan J. Travis, David W. Marshak:
Role of synaptic feedback and intrinsic voltage-gated currents in shaping cone light responses. Neurocomputing 52-54: 125-133 (2003) - 1998
- [j2]Garrett T. Kenyon, Javier F. Medina, Michael D. Mauk:
A Mathematical Model of the Cerebellar-Olivary System I: Self-Regulating Equilibrium of Climbing Fiber Activity. J. Comput. Neurosci. 5(1): 17-33 (1998) - [j1]Garrett T. Kenyon, Javier F. Medina, Michael D. Mauk:
A Mathematical Model of the Cerebellar-Olivary System II: Motor Adaptation Through Systematic Disruption of Climbing Fiber Equilibrium. J. Comput. Neurosci. 5(1): 71-90 (1998)
Conference and Workshop Papers
- 2024
- [c43]Darryl Hannan, Ragib Arnab, Gavin Parpart, Garrett T. Kenyon, Edward Kim, Yijing Watkins:
Event-To-Video Conversion for Overhead Object Detection. SSIAI 2024: 89-92 - 2023
- [c42]Gavin Parpart, Sumedh R. Risbud, Garrett T. Kenyon, Yijing Watkins:
Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor. ICONS 2023: 15:1-15:6 - [c41]Kyle Henke, Elijah Pelofske, Georg Hahn, Garrett T. Kenyon:
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor. ICONS 2023: 38:1-38:5 - 2022
- [c40]Michael A. Teti, Garrett T. Kenyon, Ben Migliori, Juston Moore:
LCANets: Lateral Competition Improves Robustness Against Corruption and Attack. ICML 2022: 21232-21252 - [c39]Gavin Parpart, Carlos González Rivera, Terrence C. Stewart, Edward Kim, Jocelyn Rego, Andrew O'Brien, Steven C. Nesbit, Garrett T. Kenyon, Yijing Watkins:
Dictionary Learning with Accumulator Neurons. ICONS 2022: 11:1-11:9 - [c38]Kyle Henke, Michael A. Teti, Garrett T. Kenyon, Ben Migliori, Gerd J. Kunde:
Apples-to-spikes: The first detailed comparison of LASSO solutions generated by a spiking neuromorphic processor. ICONS 2022: 19:1-19:8 - [c37]Steven C. Nesbit, Andrew O'Brien, Jocelyn Rego, Gavin Parpart, Carlos González Rivera, Garrett T. Kenyon, Edward Kim, Terrence C. Stewart, Yijing Watkins:
Think Fast: Time Control in Varying Paradigms of Spiking Neural Networks. ICONS 2022: 21:1-21:8 - [c36]Edward Kim, Trang Ha, Garrett T. Kenyon:
Sparse Kernel Transfer Learning. ISVC (2) 2022: 39-52 - [c35]Haydn T. Jones, Jacob M. Springer, Garrett T. Kenyon, Juston S. Moore:
If you've trained one you've trained them all: inter-architecture similarity increases with robustness. UAI 2022: 928-937 - 2021
- [c34]Jacob M. Springer, Garrett T. Kenyon:
It's Hard for Neural Networks to Learn the Game of Life. IJCNN 2021: 1-8 - [c33]Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon:
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks. NeurIPS 2021: 9759-9773 - 2020
- [c32]Nga T. T. Nguyen, Juston S. Moore, Garrett T. Kenyon:
Using models of cortical development based on sparse coding to discriminate between real and synthetically-generated faces. AIPR 2020: 1-7 - [c31]Yijing Watkins, Edward Kim, Andrew Sornborger, Garrett T. Kenyon:
Using Sinusoidally-Modulated Noise as a Surrogate for Slow-Wave Sleep to Accomplish Stable Unsupervised Dictionary Learning in a Spike-Based Sparse Coding Model. CVPR Workshops 2020: 1482-1487 - [c30]Edward Kim, Jocelyn Rego, Yijing Watkins, Garrett T. Kenyon:
Modeling Biological Immunity to Adversarial Examples. CVPR 2020: 4665-4674 - [c29]Kyle Henke, Ben Migliori, Garrett T. Kenyon:
Alien vs. Predator: Brain Inspired Sparse Coding Optimization on Neuromorphic and Quantum Devices. ICRC 2020: 26-33 - [c28]Kyle Henke, Garrett T. Kenyon, Ben Migliori:
Machine Learning in a Post Moore's Law World: Quantum vs. Neuromorphic Substrates. SSIAI 2020: 74-77 - [c27]Daniel A. Wang, Charles M. S. Strauss, Jacob M. Springer, Austin Thresher, Howard Pritchard, Garrett T. Kenyon:
Sparse MP4. SSIAI 2020: 99-103 - [c26]Michael A. Teti, Emily Meyer, Garrett T. Kenyon:
Can Lateral Inhibition for Sparse Coding Help Explain V1 Neuronal Responses To Natural Stimuli? SSIAI 2020: 120-124 - 2019
- [c25]Mohit L. Dubey, Haydn Jones, Austin Thresher, Garrett T. Kenyon:
Separating musical sources with convolutional sparse coding. APPIS 2019: 8:1-8:5 - [c24]Yijing Watkins, Austin Thresher, Peter F. Schultz, Andreas Wild, Andrew Sornborger, Garrett T. Kenyon:
Unsupervised Dictionary Learning via a Spiking Locally Competitive Algorithm. ICONS 2019: 11:1-11:5 - [c23]Edward Kim, Jessica Yarnall, Priya Shah, Garrett T. Kenyon:
A Neuromorphic Sparse Coding Defense to Adversarial Images. ICONS 2019: 12:1-12:8 - 2018
- [c22]Edward Kim, Darryl Hannan, Garrett T. Kenyon:
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons. CVPR 2018: 1111-1120 - [c21]Nga T. T. Nguyen, Garrett T. Kenyon:
Image Classification Using Quantum Inference on the D-Wave 2X. ICRC 2018: 1-7 - [c20]Nga T. T. Nguyen, Garrett T. Kenyon:
Radiographic Inference Based on a Model of V1 Simple Cells Implemented on the D-Wave 2X Quantum Annealing Computer. ICRC 2018: 1-6 - [c19]Xinhua Zhang, Yijing Watkins, Garrett T. Kenyon:
Can Deep Learning Learn the Principle of Closed Contour Detection? ISVC 2018: 455-460 - [c18]Yijing Watkins, Oleksandr Iaroshenko, Mohammad Sayeh, Garrett T. Kenyon:
Image Compression: Sparse Coding vs. Bottleneck Autoencoders. SSIAI 2018: 17-20 - 2017
- [c17]Nga T. T. Nguyen, Amy E. Larson, Garrett T. Kenyon:
Generating Sparse Representations Using Quantum Annealing: Comparison to Classical Algorithms. ICRC 2017: 1-6 - 2016
- [c16]Mohit L. Dubey, Peter F. Schultz, Garrett T. Kenyon:
Learning phase-rich features from streaming auditory images. SSIAI 2016: 73-76 - [c15]Sheng Y. Lundquist, Dylan M. Paiton, Peter F. Schultz, Garrett T. Kenyon:
Sparse encoding of binocular images for depth inference. SSIAI 2016: 121-124 - 2015
- [c14]Xinhua Zhang, Garrett T. Kenyon:
A Deconvolutional Strategy for Implementing Large Patch Sizes Supports Improved Image Classification. BICT 2015: 529-534 - [c13]Dylan M. Paiton, Sheng Y. Lundquist, William Shainin, Xinhua Zhang, Peter F. Schultz, Garrett T. Kenyon:
A Deconvolutional Competitive Algorithm for Building Sparse Hierarchical Representations. BICT 2015: 535-542 - 2013
- [c12]Will Landecker, Michael D. Thomure, Luís M. A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, Steven P. Brumby:
Interpreting individual classifications of hierarchical networks. CIDM 2013: 32-38 - [c11]Sheng Y. Lundquist, Dylan M. Paiton, Brennan M. Nowers, Peter F. Schultz, Steven P. Brumby, Anders M. Jorgensen, Garrett T. Kenyon:
Biologically inspired distributed sensor networks: Collective signal amplification via ultra-low bandwidth spike-based communication. IJCNN 2013: 1-9 - [c10]Michael D. Thomure, Melanie Mitchell, Garrett T. Kenyon:
On the role of shape prototypes in hierarchical models of vision. IJCNN 2013: 1-6 - 2012
- [c9]Wentao Huang, Zhengping Ji, Steven P. Brumby, Garrett T. Kenyon, Luís M. A. Bettencourt:
Development of invariant feature maps via a computational model of simple and complex cells. IJCNN 2012: 1-7 - [c8]Dylan M. Paiton, Steven P. Brumby, Garrett T. Kenyon, Gerd J. Kunde, Kris D. Peterson, Mohammed I. Ham, Peter F. Schultz, John S. George:
Combining multiple visual processing streams for locating and classifying objects in video. SSIAI 2012: 49-52 - [c7]Peter F. Schultz, Luís M. A. Bettencourt, Garrett T. Kenyon:
A symmetry-breaking generative model of a simple-cell/complex-cell hierarchy. SSIAI 2012: 89-92 - 2011
- [c6]Tsvi Achler, Luís M. A. Bettencourt, Garrett T. Kenyon:
Asymmetry and Similarity Phenomena in Backwards Masking Experiments Suggest Internal Reconstruction. CogSci 2011 - [c5]Zhengping Ji, Wentao Huang, Garrett T. Kenyon, Luís M. A. Bettencourt:
Hierarchical discriminative sparse coding via bidirectional connections. IJCNN 2011: 2844-2851 - 2009
- [c4]Steven P. Brumby, Garrett T. Kenyon, Will Landecker, Craig Rasmussen, Sriram Swaminarayan, Luís M. A. Bettencourt:
Large-scale functional models of visual cortex for remote sensing. AIPR 2009: 1-6 - 2004
- [c3]Mark Flynn, Henry D. I. Abarbanel, Garrett T. Kenyon:
Neurally-Based Algorithms for Image Processing. AIPR 2004: 79-85 - [c2]Alex Nugent, Garrett T. Kenyon, Reid B. Porter:
Unsupervised Adaptation to Improve Fault Tolerance of Neural Network Classifiers. Evolvable Hardware 2004: 146- - 2002
- [c1]John S. George, D. Rector, Garrett T. Kenyon, David M. Schmidt, Bryan J. Travis, C. C. Wood:
Dynamic imaging of brain function: toward convergence with neural network simulation. ISBI 2002: 1059-1062
Informal and Other Publications
- 2024
- [i22]Siddharth Mansingh, Michal Kucer, Garrett T. Kenyon, Juston Moore, Michael A. Teti:
How Robust Are Energy-Based Models Trained With Equilibrium Propagation? CoRR abs/2401.11543 (2024) - [i21]Darryl Hannan, Ragib Arnab, Gavin Parpart, Garrett T. Kenyon, Edward Kim, Yijing Watkins:
Event-to-Video Conversion for Overhead Object Detection. CoRR abs/2402.06805 (2024) - [i20]Kyle Henke, Elijah Pelofske, Garrett T. Kenyon, Georg Hahn:
Comparing Quantum Annealing and Spiking Neuromorphic Computing for Sampling Binary Sparse Coding QUBO Problems. CoRR abs/2405.20525 (2024) - 2023
- [i19]Kyle Henke, Elijah Pelofske, Georg Hahn, Garrett T. Kenyon:
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor. CoRR abs/2306.01940 (2023) - [i18]Gavin Parpart, Sumedh R. Risbud, Garrett T. Kenyon, Yijing Watkins:
Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor. CoRR abs/2307.13762 (2023) - [i17]Sayanton V. Dibbo, Juston S. Moore, Garrett T. Kenyon, Michael A. Teti:
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition. CoRR abs/2308.12882 (2023) - 2022
- [i16]Gavin Parpart, Carlos González Rivera, Terrence C. Stewart, Edward Kim, Jocelyn Rego, Andrew O'Brien, Steven C. Nesbit, Garrett T. Kenyon, Yijing Watkins:
Dictionary Learning with Accumulator Neurons. CoRR abs/2205.15386 (2022) - 2021
- [i15]Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon:
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers. CoRR abs/2102.05110 (2021) - [i14]Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon:
A Little Robustness Goes a Long Way: Leveraging Universal Features for Targeted Transfer Attacks. CoRR abs/2106.02105 (2021) - [i13]Boram Yoon, Chia-Cheng Chang, Garrett T. Kenyon, Nga T. T. Nguyen, Ermal Rrapaj:
Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer. CoRR abs/2112.02120 (2021) - [i12]Michal Kucer, Diane Oyen, Garrett T. Kenyon:
On visual self-supervision and its effect on model robustness. CoRR abs/2112.04367 (2021) - 2020
- [i11]Jacob M. Springer, Garrett T. Kenyon:
It's Hard for Neural Networks To Learn the Game of Life. CoRR abs/2009.01398 (2020) - [i10]Edward Kim, Maryam Daniali, Jocelyn Rego, Garrett T. Kenyon:
The Selectivity and Competition of the Mind's Eye in Visual Perception. CoRR abs/2011.11167 (2020) - 2019
- [i9]Nga T. T. Nguyen, Garrett T. Kenyon:
Image classification using quantum inference on the D-Wave 2X. CoRR abs/1905.13215 (2019) - 2018
- [i8]Jacob M. Springer, Charles M. S. Strauss, Austin M. Thresher, Edward Kim, Garrett T. Kenyon:
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples. CoRR abs/1811.07211 (2018) - 2017
- [i7]Sheng Y. Lundquist, Melanie Mitchell, Garrett T. Kenyon:
Sparse Coding on Stereo Video for Object Detection. CoRR abs/1705.07144 (2017) - [i6]Jacob Carroll, Nils Carlson, Garrett T. Kenyon:
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks. CoRR abs/1710.09875 (2017) - [i5]Yijing Watkins, Mohammad Sayeh, Oleksandr Iaroshenko, Garrett T. Kenyon:
Image Compression: Sparse Coding vs. Bottleneck Autoencoders. CoRR abs/1710.09926 (2017) - [i4]Mohit L. Dubey, Garrett T. Kenyon, Nils Carlson, Austin Thresher:
Does Phase Matter For Monaural Source Separation? CoRR abs/1711.00913 (2017) - [i3]Edward Kim, Darryl Hannan, Garrett T. Kenyon:
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons. CoRR abs/1711.07998 (2017) - 2016
- [i2]Sunil Thulasidasan, Jeffrey A. Bilmes, Garrett T. Kenyon:
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs. CoRR abs/1612.04898 (2016) - 2014
- [i1]Peter F. Schultz, Dylan M. Paiton, Wei Lu, Garrett T. Kenyon:
Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels. CoRR abs/1406.4205 (2014)
Coauthor Index
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