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.

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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.

Recent Publications

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.

Recent Presentations and Posters

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


Edward Kim

Assistant Professor

Jenish Maharjan

Masters Student

Jessica Yarnall

Masters Student

Priya Shah

Masters Student

Jocelyn Rego

Masters Student

Rahul Thapa

Undergraduate Student

Shiyu Su

Undergraduate Student

Joselyn Penafiel

Undergraduate Student

Zach DeStefano

Undergraduate Student

Sophia Tong

Undergraduate Student

Käthe Specht

Undergraduate Student

Peter Lyu

Undergraduate Student

Billy Lu

Undergraduate Student


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



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.