Publications

Vineyard, Craig M.; Verzi, Stephen J.; Taylor, Shawn E.; Dubicka, Irene D.; Bernard, Michael L.

This report describes the laboratory directed research and development work to model relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism to support rapid decision making. Using the biology and functionality of the hippocampus as an analogy or inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations form. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a novel approach to data mining for knowledge discovery. This report describes the neurobiological inspiration, architecture, and operational characteristics of our model, and also provides a real world terrorist network example to illustrate the model's functionality.

Bernard, Michael L.; Verzi, Stephen J.; Taylor, Shawn E.; Dubicka, Irene D.; McClain, Jonathan T.; Vineyard, Craig M.

Abstract not provided.

Bernard, Michael L.; Dubicka, Irene D.; McClain, Jonathan T.; Taylor, Shawn E.; Verzi, Stephen J.; Vineyard, Craig M.

Abstract not provided.

Bernard, Michael L.; Morrow, James D.; Taylor, Shawn E.; Verzi, Stephen J.; Vineyard, Craig M.

Working with leading experts in the field of cognitive neuroscience and computational intelligence, SNL has developed a computational architecture that represents neurocognitive mechanisms associated with how humans remember experiences in their past. The architecture represents how knowledge is organized and updated through information from individual experiences (episodes) via the cortical-hippocampal declarative memory system. We compared the simulated behavioral characteristics with those of humans measured under well established experimental standards, controlling for unmodeled aspects of human processing, such as perception. We used this knowledge to create robust simulations of & human memory behaviors that should help move the scientific community closer to understanding how humans remember information. These behaviors were experimentally validated against actual human subjects, which was published. An important outcome of the validation process will be the joining of specific experimental testing procedures from the field of neuroscience with computational representations from the field of cognitive modeling and simulation.

Taylor, Shawn E.; Bernard, Michael L.; Vineyard, Craig M.; Verzi, Stephen J.; Morrow, James D.

Abstract not provided.