Publications

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; Aimone, James B.

Abstract not provided.

Proceedings of the International Joint Conference on Neural Networks

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; Aimone, James B.

Through various means of structural and synaptic plasticity enabling online learning, neural networks are constantly reconfiguring their computational functionality. Neural information content is embodied within the configurations, representations, and computations of neural networks. To explore neural information content, we have developed metrics and computational paradigms to quantify neural information content. We have observed that conventional compression methods may help overcome some of the limiting factors of standard information theoretic techniques employed in neuroscience, and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles. Using Lempel-Ziv compression we are able to assess the rate of generation of new patterns across a neural ensemble's firing activity over time to approximate the information content encoded by a neural circuit. As a specific case study, we have been investigating the effect of neural mixed coding schemes due to hippocampal adult neurogenesis.

Vugrin, Eric D.; Trucano, Timothy G.; Swiler, Laura P.; Finley, Patrick D.; Flanagan, Tatiana P.; Naugle, Asmeret B.; Tsao, Jeffrey Y.; Verzi, Stephen J.

Improved validation for models of complex systems has been a primary focus over the past year for the Resilience in Complex Systems Research Challenge. This document describes a set of research directions that are the result of distilling those ideas into three categories of research -- epistemic uncertainty, strong tests, and value of information. The content of this document can be used to transmit valuable information to future research activities, update the Resilience in Complex Systems Research Challenge's roadmap, inform the upcoming FY18 Laboratory Directed Research and Development (LDRD) call and research proposals, and facilitate collaborations between Sandia and external organizations. The recommended research directions can provide topics for collaborative research, development of proposals, workshops, and other opportunities.

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; Aimone, James B.; Heileman, Gregory L.

Abstract not provided.

Proceedings of the International Joint Conference on Neural Networks

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; Aimone, James B.; Heileman, Gregory L.

The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. An added challenge arises when the problem domain is dynamic or non-stationary with the data distributions or categorizations changing over time. This phenomenon is known as concept drift. Game-theoretic algorithms are often iterative by nature, consisting of repeated game play rather than a single interaction. Effectively, rather than requiring extensive retraining to update a learning model, a game-theoretic approach can adjust strategies as a novel approach to concept drift. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in an adaptive manner with repeated play to address concept drift, and show results of applying this algorithm to synthetic as well as real data.

Backus, George A.; Naugle, Asmeret B.; Bernard, Michael L.; Aamir, Munaf S.; Glickman, Matthew R.; Jeffers, Robert F.; Silva, Austin R.; Trumbo, Derek T.; Verzi, Stephen J.

Abstract not provided.

Bernard, Michael L.; Glickman, Matthew R.; Hart, Derek H.; Xavier, Patrick G.; Verzi, Stephen J.; Wolfenbarger, Paul W.

This 3-year research and development effort focused on what we believe is a significant technical gap in existing modeling and simulation capabilities: the representation of plausible human cognition and behaviors within a dynamic, simulated environment. Specifically, the intent of the ''Simulating Human Behavior for National Security Human Interactions'' project was to demonstrate initial simulated human modeling capability that realistically represents intra- and inter-group interaction behaviors between simulated humans and human-controlled avatars as they respond to their environment. Significant process was made towards simulating human behaviors through the development of a framework that produces realistic characteristics and movement. The simulated humans were created from models designed to be psychologically plausible by being based on robust psychological research and theory. Progress was also made towards enhancing Sandia National Laboratories existing cognitive models to support culturally plausible behaviors that are important in representing group interactions. These models were implemented in the modular, interoperable, and commercially supported Umbra{reg_sign} simulation framework.

Vineyard, Craig M.; Verzi, Stephen J.; Severa, William M.; Aimone, James B.

Abstract not provided.

Verzi, Stephen J.; Dellana, Ryan A.; Severa, William M.; Smith, Michael R.; Rothganger, Fredrick R.; James, Conrad D.; Aimone, James B.; Vineyard, Craig M.

Abstract not provided.

Outkin, Alexander V.; Eames, Brandon K.; Jones, Todd J.; Vugrin, Eric D.; Phillips, Cynthia A.; Walsh, Sarah W.; Hobbs, Jacob A.; Gearhart, Jared L.; Siirola, John D.; Verzi, Stephen J.; Naugle, Asmeret B.

Abstract not provided.

Outkin, Alexander V.; Eames, Brandon K.; Jones, Todd J.; Vugrin, Eric D.; Phillips, Cynthia A.; Walsh, Sarah W.; Hobbs, Jacob A.; Gearhart, Jared L.; Siirola, John D.; Verzi, Stephen J.; Naugle, Asmeret B.

Abstract not provided.

Thorpe, Jamie T.; Sahakian, Meghan A.; Musuvathy, Srideep M.; Verzi, Stephen J.; Dykstra, Matthew D.; Vugrin, Eric D.

Abstract not provided.

Sahakian, Meghan A.; Musuvathy, Srideep M.; Thorpe, Jamie T.; Verzi, Stephen J.; Vugrin, Eric D.; Dykstra, Matthew D.

Abstract not provided.

Sahakian, Meghan A.; Musuvathy, Srideep M.; Thorpe, Jamie T.; Verzi, Stephen J.; Vugrin, Eric D.; Dykstra, Matthew D.

Abstract not provided.

Bernard, Michael L.; Backus, George A.; Glickman, Matthew R.; Verzi, Stephen J.

Abstract not provided.

Naugle, Asmeret B.; Bernard, Michael L.; Backus, George A.; Glickman, Matthew R.; Verzi, Stephen J.

Abstract not provided.

Aimone, James B.; Bernard, Michael L.; Vineyard, Craig M.; Verzi, Stephen J.

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

Computational and Mathematical Organization Theory

Naugle, Asmeret B.; Krofcheck, Daniel J.; Warrender, Christina E.; Lakkaraju, Kiran L.; Swiler, Laura P.; Verzi, Stephen J.; Emery, Ben; Murdock, Jaimie; Bernard, Michael L.; Romero, Vicente J.

The ground truth program used simulations as test beds for social science research methods. The simulations had known ground truth and were capable of producing large amounts of data. This allowed research teams to run experiments and ask questions of these simulations similar to social scientists studying real-world systems, and enabled robust evaluation of their causal inference, prediction, and prescription capabilities. We tested three hypotheses about research effectiveness using data from the ground truth program, specifically looking at the influence of complexity, causal understanding, and data collection on performance. We found some evidence that system complexity and causal understanding influenced research performance, but no evidence that data availability contributed. The ground truth program may be the first robust coupling of simulation test beds with an experimental framework capable of teasing out factors that determine the success of social science research.

Dunlavy, Daniel D.; Basilico, Justin D.; Verzi, Stephen J.; Bauer, Travis L.

This report describes the Licensing Support Network (LSN) Assistant--a set of tools for categorizing e-mail messages and documents, and investigating and correcting existing archives of categorized e-mail messages and documents. The two main tools in the LSN Assistant are the LSN Archive Assistant (LSNAA) tool for recategorizing manually labeled e-mail messages and documents and the LSN Realtime Assistant (LSNRA) tool for categorizing new e-mail messages and documents. This report focuses on the LSNAA tool. There are two main components of the LSNAA tool. The first is the Sandia Categorization Framework, which is responsible for providing categorizations for documents in an archive and storing them in an appropriate Categorization Database. The second is the actual user interface, which primarily interacts with the Categorization Database, providing a way for finding and correcting categorizations errors in the database. A procedure for applying the LSNAA tool and an example use case of the LSNAA tool applied to a set of e-mail messages are provided. Performance results of the categorization model designed for this example use case are presented.