Sparse Coding Lab
SPiking And Recurrent SoftwarE Coding Lab @ Villanova University
Research inspired by breakthroughs in computational and theoretical neuroscience that incorporate ideas not explored by current feed-forward deep learning architectures.Learn more
112 dictionary elements learned after viewing 50,000 CIFAR-10 images.
We are exploring AI frameworks that mimic how the mammalian brain senses and understands the world. Our goal is to develop an AI system will learn much like an infant learns, by simply observing the world and learning through observation. Eventually, the model should learn the structure of the world and existing associations, and accurately make predictions. We are using neuromorphic software and hardware concepts such as sparse coding, top-down feedback, spiking neural networks, and neuronal dynamics to create a machine intelligence that has a better understanding of the world in which we live.
E.Kim, J.Yarnall, P.Shah, G.Kenyon, "A Neuromorphic Sparse Coding Defense to Adversarial Images", International Conference on Neuromorphic Systems, ICONS, 2019.
Y.Watkins, A.Thresher, P.Schultz, A.Wild, A.Sornborger, E.Kim, G.Kenyon, "Towards Self-Organizing Neuromorphic Processors: Unsupervised Dictionary Learning via a Spiking Locally Competitive Algorithm", International Conference on Neuromorphic Systems, ICONS, 2019.
J.Springer, C.Strauss, A.Thresher, E.Kim, G.Kenyon, "Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples", arXiv:1811.07211, 2018.
E.Kim, K.McCoy, "Multimodal Deep Learning using Images and Text for Information Graphic Classification", ACM SIGACCESS Conference on Computers and Accessibility, Assets, 2018 (Best Paper Nominee).
E.Kim, D.Hannan, G.Kenyon, "Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons", International Conference on Computer Vision and Pattern Recognition, CVPR, 2018.
J.Yarnall, P.Shah, E.Kim, "A Neuromorphic Sparse Coding Defense to Adversarial Images", Sigma Xi Student Research Poster Symposium, Villanova, 2019
E.Kim, E.Lawson, K.Sullivan, G.Kenyon, "Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding", Neuro-inspired Computational Elements Workshop, NICE, 2019
Garrett T. Kenyon, Ph.D. - Los Alamos National Labratory
Yijing Watkins, Ph.D. - Los Alamos National Labratory
Ed Lawson, Ph.D. - Naval Research Labratory
Keith Sullivan, Ph.D. - Naval Research Labratory
Kathleen McCoy, Ph.D. - University of Delaware
Darryl Hannan - University of North Carolina, Ph.D. student
Jacob Springer - Swarthmore Undergraduate student
Hebbian Learning - Jocelyn Rego
HMax Model - Jenish Maharjan
Hopfield Networks - Jessica Yarnall
Distributions - Peter Lyu and Kathe Specht
Basics of Linear Algebra - Joselyn Penafiel and Sophia Tong
Spike Timing Dependent Plasticity - Jocelyn Rego
Backpropagation - Peter Lyu and Billy Lu
This material is based upon work supported by the National Science Foundation under Grant No. 1846023
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the National Science Foundation.