Dynamical Memory in Deep Neural Networks -

Abstract

In this work, I will begin to lay out a roadmap or framework for which I believe will serve the scientific communities of artificial intelligence and cognitive neuroscience of interest, in future development and design of a thinking intelligent machine, based on the accumulated knowledge I have gathered across many sources: from my advisors, peers and colleagues, collaborators, talks, symposia and conferences, and long paper dives, for the almost decade that I have spent at my new home in College Park, Maryland. It is my hope and intent that this thesis serves in its stated goal to advance the science of memory integration in neural networks, but in addition, to further the distant dream of discovering the mystery of what it means to be alive. It is important to note that while this thesis is focused on the critical integration of memory mechanisms into artificial neural networks, the authors’ larger goal is the creation of an overarching cognitive architecture that takes advantages of the right amount of advances from deep learning, with the right amount of insights from cognitive and neuroscience - a ”Goldilocks” of sorts for AI. It is my hope that through understanding mechanisms of memory and how they interact with our stimluli, we move one step closer to understanding our place in the cosmos.