Google Scholar Page



Architecture

Joshua Bowren
Graduate Student
Computational Neuroscience
University of Miami

jbowren@miami.edu

About

Curiculum Vitae (CV)

I am a graduate student at the University of Miami working in the lab of Dr. Odelia Schwartz since the Fall of 2018. My main research interests are computational neuroscience, vision, sparse coding, and machine learning. My work is supported by a National Science Foundation (NSF) Graduate Research Fellowship.

I recieved a B.S. in computer science with a minor in mathematics from the University of Central Florida. During my time at UCF I worked with Dr. Kenneth Stanley on an neural network called a Real-Time Autoencoder-Augmented Hebbian Network (RAAHN) which addresses control tasks. In 2016 I worked with Dr. R. Paul Wiegand of the Institute for Simulation and Training at UCF on brain-inspired image compression. During the summer of 2017 I worked at the Princeton Neuroscience Institute in the lab of Dr. Jonathan Pillow on hierarchical and non-negative sparse coding.

Projects

Positions

University of Miami

University of Miami

Research Assistant

Advisor: Dr. Odelia Schwartz

June 2018 – Present

Hierarchical models of visual processing

Institute for Simulation and Training

Institute for Simulation and Training at UCF

Research Assistant

Advisor: Dr. R. Paul Wiegand

August 2016 – May 2018

Image Compression

Princeton Neuroscience Institute

Princeton Neuroscience Institute

Research Intern

Advisor: Dr. Jonathan Pillow

June 2017 – August 2017

Hierarchical and Non-Negative Sparse Coding.

University of Central Florida

University of Central Florida

Research Assistant

Advisor: Dr. Kenneth Stanley

June 2014 – May 2017

Real-Time Autoencoder-Augmented Hebbian Network

Publications

(2021) A Sparse Coding Interpretation of Neural Networks and Theoretical Implications

Joshua Bowren arXiv.

(2022) Inference via Sparse Coding in a Hierarchical Vision Model

Joshua Bowren, Luis Sanchez-Giraldo, Odelia Schwartz; Inference via sparse coding in a hierarchical vision model. Journal of Vision 2022;22(2):19. doi: https://doi.org/10.1167/jov.22.2.19.

(2016) Fully Autonomous Real-Time Autoencoder-Augmented Hebbian Learning through the Collection of Novel Experiences

Joshua A. Bowren, Justin, K. Pugh, and Kenneth O. Stanley In: Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XV). Cambridge, MA: MIT Press, 2016. 8 pages.