Rowan University Rowan Digital Works Theses and Dissertations 5-24-2018 An investigation of the cortical learning algorithm Anthony C. Samaritano Rowan University Follow this and additional works at: https://rdw.rowan.edu/etd Part of the Electrical and Computer Engineering Commons, and the Neuroscience and Neurobiology Commons Recommended Citation Samaritano, Anthony C., "An investigation of the cortical learning algorithm" (2018). Theses and Dissertations. 2572. https://rdw.rowan.edu/etd/2572 This Thesis is brought to you for free and open access by Rowan Digital Works. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Rowan Digital Works. For more information, please contact [email protected]. AN INVESTIGATION OF THE CORTICAL LEARNING ALGORITHM by Anthony C. Samaritano A Thesis Submitted to the Department of Electrical and Computer Engineering College of Engineering In partial fulfillment of the requirement For the degree of Master of Science in Electrical and Computer Engineering at Rowan University November 2, 2016 Thesis Advisor: Robi Polikar, Ph.D. © 2018 Anthony C. Samaritano Acknowledgments I would like to express my sincerest gratitude and appreciation to Dr. Robi Polikar for his help, instruction, and patience throughout the development of this thesis and my research into cortical learning algorithms. Dr. Polikar took a risk by allowing me to follow my passion for neurobiologically inspired algorithms and explore this emerging category of cortical learning algorithms in the machine learning field. The skills and knowledge I acquired throughout my research have definitively molded me into a more diligent and thorough engineer. I have, and will continue to, take these characteristics and skills I have gained into my current and future professional endeavors. I would like to thank my mother, Dawn Samaritano, and fiancé, Lauren Anderson, for the continued and unwavering love and support through this journey. Without your support, I would not have been about to finish this research. Thank you for propelling me forward at every turn and the countless hours of reading and feedback throughout the writing of the thesis. iii Abstract Anthony C. Samaritano AN INVESTIGATION OF THE CORTICAL LEARNING ALGORITHM 2017-2018 Robi Polikar, Ph.D. Master of Science in Electrical and Computer Engineering Pattern recognition and machine learning fields have revolutionized countless industries and applications from biometric security to modern industrial assembly lines. The fields continue to accelerate as faster, more efficient processing hardware becomes commercially available. Despite the accelerated growth of the pattern recognition and machine learning fields, computers still are unable to learn, reason, and perform rudimentary tasks that humans and animals find routine. Animals are able to move fluidly, understand their environment, and maximize their chances of survival through adaptation – animals demonstrate intelligence. A primary argument in this thesis that we have not yet achieved a level of intelligence similar to humans and animals in the pattern recognition and machine learning fields, not due to a lack of computational power but, rather, due to lack of understanding of how the cortical structures of mammalian brain interact and operate. This thesis describes a cortical learning algorithm (CLA) that models how the cortical structures in the mammalian neocortex operate. Furthermore, a high level understanding of how the cortical structures in the mammalian brain interact, store semantic patterns, and auto-recall these patterns for future predictions are discussed. Finally, we demonstrate that the algorithm can build and maintain a model of its environment and provide feedback for actions and/or classification in a similar fashion to our understanding of cortical operation. iv Table of Contents Abstract .......................................................................................................................... iv List of Figures ................................................................................................................ ix List of Tables............................................................................................................... xiii Introduction .................................................................................................... 1 Why Study the Brain? ................................................................................................ 2 Related Work ............................................................................................................. 4 Motivation ................................................................................................................. 5 Contributions ............................................................................................................. 6 Broader Impacts......................................................................................................... 7 Thesis Structure ......................................................................................................... 7 Understanding the Brain ................................................................................. 9 Cortical Structures ..................................................................................................... 9 Neuron ................................................................................................................. 9 Neocortex .......................................................................................................... 13 Neocortical Structure ......................................................................................... 14 Neocortical Regions ........................................................................................... 18 Thalamus ........................................................................................................... 19 Hippocampus ..................................................................................................... 22 Machine Learning......................................................................................... 25 Supervised Learning ................................................................................................ 25 Reinforcement Learning .......................................................................................... 27 Unsupervised Learning ............................................................................................ 28 v Table of Contents (continued) Applicable Machine Learning Algorithms ............................................................... 29 Shallow Architectures ........................................................................................ 30 Deep Architectures ............................................................................................. 35 Relationship between the Brain and Machine Learning ................................. 40 Properties of the Brain ............................................................................................. 40 Online Learning ................................................................................................. 41 Hierarchy ........................................................................................................... 42 Time Sequences ................................................................................................. 45 Sparsity .............................................................................................................. 47 Attention ............................................................................................................ 49 Invariant Representation .................................................................................... 52 Signal Agnostics and Plasticity. ......................................................................... 54 Pattern Recognition Properties of an Intelligent System ........................................... 56 Resistance to Catastrophic Forgetting ................................................................. 56 Data Fusion ........................................................................................................ 57 Noise Resistance ................................................................................................ 58 Anomaly Detection ............................................................................................ 60 Non-Stationary Environments ............................................................................ 61 New Class .......................................................................................................... 62 Cortical Learning Algorithm ......................................................................... 64 Neocortical Principles .............................................................................................. 65 Online Learning and Time Sequences ................................................................ 65 vi Table of Contents (continued) Cell Regions and Hierarchy................................................................................ 65 Sparsity .............................................................................................................. 66 Prediction and Invariant Representations ............................................................ 67 Cortical Learning Algorithm Design ........................................................................ 68 Modularity ........................................................................................................